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32 changed files with 5698 additions and 2816 deletions
58
.github/workflows/build.yml
vendored
58
.github/workflows/build.yml
vendored
|
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@ -1,58 +0,0 @@
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|||
name: Build CI
|
||||
|
||||
on:
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push:
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||||
pull_request:
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release:
|
||||
types: [published]
|
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check_suite:
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||||
types: [rerequested]
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|
||||
jobs:
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test:
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runs-on: ubuntu-16.04
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steps:
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- name: Dump GitHub context
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env:
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GITHUB_CONTEXT: ${{ toJson(github) }}
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run: echo "$GITHUB_CONTEXT"
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- name: Set up Python 3.5
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uses: actions/setup-python@v1
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with:
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python-version: 3.5
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||||
|
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- name: Versions
|
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run: |
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gcc --version
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python3 --version
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- name: Checkout ulab
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uses: actions/checkout@v1
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||||
|
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- name: Checkout micropython repo
|
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uses: actions/checkout@v2
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with:
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repository: micropython/micropython
|
||||
path: micropython
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||||
|
||||
- name: Checkout micropython submodules
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||||
run: (cd micropython && git submodule update --init)
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||||
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- name: Build mpy-cross
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run: make -C micropython/mpy-cross -j2
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- name: Build micropython unix port
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run: |
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make -C micropython/ports/unix -j2 deplibs
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make -C micropython/ports/unix -j2 USER_C_MODULES=$(readlink -f .)
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- name: Run tests
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run: env MICROPYTHON_CPYTHON3=python3.5 MICROPY_MICROPYTHON=micropython/ports/unix/micropython micropython/tests/run-tests -d tests
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- name: Print failure info
|
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run: |
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for exp in *.exp;
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do testbase=$(basename $exp .exp);
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echo -e "\nFAILURE $testbase";
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diff -u $testbase.exp $testbase.out;
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done
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if: failure()
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|
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64
README.md
64
README.md
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@ -1,62 +1,12 @@
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# micropython-ulab
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# circuitpython-ulab
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|
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ulab is a numpy-like array manipulation library for micropython.
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||||
The module is written in C, defines compact containers for numerical
|
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data, and is fast.
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||||
data, and is fast.
|
||||
|
||||
Documentation can be found under https://micropython-ulab.readthedocs.io/en/latest/
|
||||
The source for the manual is in https://github.com/v923z/micropython-ulab/blob/master/docs/ulab-manual.ipynb,
|
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while developer help is in https://github.com/v923z/micropython-ulab/blob/master/docs/ulab.ipynb.
|
||||
ulab will be incorporated in builds of most CircuitPython supported
|
||||
devices, so there's usually no need to use the files here directly.
|
||||
If you've encountered a problem with circuitpython-ulab, please
|
||||
file an issue [in the circuitpython issue tracker](https://github.com/adafruit/circuitpython).
|
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|
||||
# Firmware
|
||||
|
||||
Firmware for pyboard.v.1.1, and PYBD_SF6 is updated once in a while, and can be downloaded
|
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from https://github.com/v923z/micropython-ulab/releases.
|
||||
|
||||
## Compiling
|
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|
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If you want to try the latest version of `ulab`, or your hardware is
|
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different to pyboard.v.1.1, or PYBD_SF6, the firmware can be compiled
|
||||
from the source by following these steps:
|
||||
|
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First, you have to clone the micropython repository by running
|
||||
|
||||
```
|
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git clone https://github.com/micropython/micropython.git
|
||||
```
|
||||
on the command line. This will create a new repository with the name `micropython`. Staying there, clone the `ulab` repository with
|
||||
|
||||
```
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git clone https://github.com/v923z/micropython-ulab.git ulab
|
||||
```
|
||||
|
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Then you have to include `ulab` in the compilation process by editing `mpconfigport.h` of the directory of the port for which you want to compile, so, still on the command line, navigate to `micropython/ports/unix`, or `micropython/ports/stm32`, or whichever port is your favourite, and edit the `mpconfigport.h` file there. All you have to do is add a single line at the end:
|
||||
|
||||
```
|
||||
#define MODULE_ULAB_ENABLED (1)
|
||||
```
|
||||
|
||||
This line will inform the compiler that you want `ulab` in the resulting firmware. If you don't have the cross-compiler installed, your might want to do that now, for instance on Linux by executing
|
||||
|
||||
```
|
||||
sudo apt-get install gcc-arm-none-eabi
|
||||
```
|
||||
If that was successful, you can try to run the make command in the port's directory as
|
||||
```
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make BOARD=PYBV11 USER_C_MODULES=../../../ulab all
|
||||
```
|
||||
which will prepare the firmware for pyboard.v.11. Similarly,
|
||||
```
|
||||
make BOARD=PYBD_SF6 USER_C_MODULES=../../../ulab all
|
||||
```
|
||||
will compile for the SF6 member of the PYBD series. Provided that you managed to compile the firmware, you would upload that by running
|
||||
either
|
||||
```
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||||
dfu-util --alt 0 -D firmware.dfu
|
||||
```
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||||
or
|
||||
```
|
||||
python pydfu.py -u firmware.dfu
|
||||
```
|
||||
|
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In case you got stuck somewhere in the process, a bit more detailed instructions can be found under https://github.com/micropython/micropython/wiki/Getting-Started, and https://github.com/micropython/micropython/wiki/Pyboard-Firmware-Update.
|
||||
circuitpython-ulab is based on [micropython-ulab](https://github.com/v923z/micropython-ulab).
|
||||
|
|
|
|||
193
code/__init__.c
Normal file
193
code/__init__.c
Normal file
|
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@ -0,0 +1,193 @@
|
|||
/*
|
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* This file is part of the micropython-ulab project,
|
||||
*
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* https://github.com/v923z/micropython-ulab
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*
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* The MIT License (MIT)
|
||||
*
|
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* Copyright (c) 2019 Zoltán Vörös
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*/
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#include <math.h>
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#include <stdio.h>
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#include <stdlib.h>
|
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#include <string.h>
|
||||
#include "py/runtime.h"
|
||||
#include "py/binary.h"
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#include "py/obj.h"
|
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#include "py/objarray.h"
|
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|
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#include "shared-bindings/ulab/ndarray.h"
|
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#include "shared-bindings/ulab/linalg.h"
|
||||
#include "shared-bindings/ulab/vectorise.h"
|
||||
#include "shared-bindings/ulab/poly.h"
|
||||
#include "shared-bindings/ulab/fft.h"
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#include "shared-bindings/ulab/numerical.h"
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|
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#define ULAB_VERSION 0.262
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|
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typedef struct _mp_obj_float_t {
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mp_obj_base_t base;
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||||
mp_float_t value;
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} mp_obj_float_t;
|
||||
|
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mp_obj_float_t ulab_version = {{&mp_type_float}, ULAB_VERSION};
|
||||
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(ndarray_shape_obj, ndarray_shape);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(ndarray_rawsize_obj, ndarray_rawsize);
|
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MP_DEFINE_CONST_FUN_OBJ_KW(ndarray_flatten_obj, 1, ndarray_flatten);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(ndarray_asbytearray_obj, ndarray_asbytearray);
|
||||
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(linalg_transpose_obj, linalg_transpose);
|
||||
MP_DEFINE_CONST_FUN_OBJ_2(linalg_reshape_obj, linalg_reshape);
|
||||
MP_DEFINE_CONST_FUN_OBJ_KW(linalg_size_obj, 1, linalg_size);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(linalg_inv_obj, linalg_inv);
|
||||
MP_DEFINE_CONST_FUN_OBJ_2(linalg_dot_obj, linalg_dot);
|
||||
MP_DEFINE_CONST_FUN_OBJ_KW(linalg_zeros_obj, 0, linalg_zeros);
|
||||
MP_DEFINE_CONST_FUN_OBJ_KW(linalg_ones_obj, 0, linalg_ones);
|
||||
MP_DEFINE_CONST_FUN_OBJ_KW(linalg_eye_obj, 0, linalg_eye);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(linalg_det_obj, linalg_det);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(linalg_eig_obj, linalg_eig);
|
||||
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_acos_obj, vectorise_acos);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_acosh_obj, vectorise_acosh);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_asin_obj, vectorise_asin);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_asinh_obj, vectorise_asinh);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_atan_obj, vectorise_atan);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_atanh_obj, vectorise_atanh);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_ceil_obj, vectorise_ceil);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_cos_obj, vectorise_cos);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_erf_obj, vectorise_erf);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_erfc_obj, vectorise_erfc);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_exp_obj, vectorise_exp);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_expm1_obj, vectorise_expm1);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_floor_obj, vectorise_floor);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_gamma_obj, vectorise_gamma);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_lgamma_obj, vectorise_lgamma);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_log_obj, vectorise_log);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_log10_obj, vectorise_log10);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_log2_obj, vectorise_log2);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_sin_obj, vectorise_sin);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_sinh_obj, vectorise_sinh);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_sqrt_obj, vectorise_sqrt);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_tan_obj, vectorise_tan);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_tanh_obj, vectorise_tanh);
|
||||
|
||||
STATIC MP_DEFINE_CONST_FUN_OBJ_KW(numerical_linspace_obj, 2, numerical_linspace);
|
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STATIC MP_DEFINE_CONST_FUN_OBJ_KW(numerical_sum_obj, 1, numerical_sum);
|
||||
STATIC MP_DEFINE_CONST_FUN_OBJ_KW(numerical_mean_obj, 1, numerical_mean);
|
||||
STATIC MP_DEFINE_CONST_FUN_OBJ_KW(numerical_std_obj, 1, numerical_std);
|
||||
STATIC MP_DEFINE_CONST_FUN_OBJ_KW(numerical_min_obj, 1, numerical_min);
|
||||
STATIC MP_DEFINE_CONST_FUN_OBJ_KW(numerical_max_obj, 1, numerical_max);
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||||
STATIC MP_DEFINE_CONST_FUN_OBJ_KW(numerical_argmin_obj, 1, numerical_argmin);
|
||||
STATIC MP_DEFINE_CONST_FUN_OBJ_KW(numerical_argmax_obj, 1, numerical_argmax);
|
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STATIC MP_DEFINE_CONST_FUN_OBJ_KW(numerical_roll_obj, 2, numerical_roll);
|
||||
STATIC MP_DEFINE_CONST_FUN_OBJ_KW(numerical_flip_obj, 1, numerical_flip);
|
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STATIC MP_DEFINE_CONST_FUN_OBJ_KW(numerical_diff_obj, 1, numerical_diff);
|
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STATIC MP_DEFINE_CONST_FUN_OBJ_KW(numerical_sort_obj, 1, numerical_sort);
|
||||
STATIC MP_DEFINE_CONST_FUN_OBJ_KW(numerical_sort_inplace_obj, 1, numerical_sort_inplace);
|
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STATIC MP_DEFINE_CONST_FUN_OBJ_KW(numerical_argsort_obj, 1, numerical_argsort);
|
||||
|
||||
STATIC MP_DEFINE_CONST_FUN_OBJ_2(poly_polyval_obj, poly_polyval);
|
||||
STATIC MP_DEFINE_CONST_FUN_OBJ_VAR_BETWEEN(poly_polyfit_obj, 2, 3, poly_polyfit);
|
||||
|
||||
STATIC MP_DEFINE_CONST_FUN_OBJ_VAR_BETWEEN(fft_fft_obj, 1, 2, fft_fft);
|
||||
STATIC MP_DEFINE_CONST_FUN_OBJ_VAR_BETWEEN(fft_ifft_obj, 1, 2, fft_ifft);
|
||||
STATIC MP_DEFINE_CONST_FUN_OBJ_VAR_BETWEEN(fft_spectrum_obj, 1, 2, fft_spectrum);
|
||||
|
||||
STATIC const mp_rom_map_elem_t ulab_ndarray_locals_dict_table[] = {
|
||||
{ MP_ROM_QSTR(MP_QSTR_shape), MP_ROM_PTR(&ndarray_shape_obj) },
|
||||
{ MP_ROM_QSTR(MP_QSTR_rawsize), MP_ROM_PTR(&ndarray_rawsize_obj) },
|
||||
{ MP_ROM_QSTR(MP_QSTR_flatten), MP_ROM_PTR(&ndarray_flatten_obj) },
|
||||
{ MP_ROM_QSTR(MP_QSTR_asbytearray), MP_ROM_PTR(&ndarray_asbytearray_obj) },
|
||||
{ MP_ROM_QSTR(MP_QSTR_transpose), MP_ROM_PTR(&linalg_transpose_obj) },
|
||||
{ MP_ROM_QSTR(MP_QSTR_reshape), MP_ROM_PTR(&linalg_reshape_obj) },
|
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{ MP_ROM_QSTR(MP_QSTR_sort), MP_ROM_PTR(&numerical_sort_inplace_obj) },
|
||||
};
|
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|
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STATIC MP_DEFINE_CONST_DICT(ulab_ndarray_locals_dict, ulab_ndarray_locals_dict_table);
|
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|
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const mp_obj_type_t ulab_ndarray_type = {
|
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{ &mp_type_type },
|
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.name = MP_QSTR_ndarray,
|
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.print = ndarray_print,
|
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.make_new = ndarray_make_new,
|
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.subscr = ndarray_subscr,
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.getiter = ndarray_getiter,
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.unary_op = ndarray_unary_op,
|
||||
.binary_op = ndarray_binary_op,
|
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.locals_dict = (mp_obj_dict_t*)&ulab_ndarray_locals_dict,
|
||||
};
|
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|
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STATIC const mp_map_elem_t ulab_globals_table[] = {
|
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{ MP_OBJ_NEW_QSTR(MP_QSTR___name__), MP_OBJ_NEW_QSTR(MP_QSTR_ulab) },
|
||||
{ MP_ROM_QSTR(MP_QSTR___version__), MP_ROM_PTR(&ulab_version) },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_array), (mp_obj_t)&ulab_ndarray_type },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_size), (mp_obj_t)&linalg_size_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_inv), (mp_obj_t)&linalg_inv_obj },
|
||||
{ MP_ROM_QSTR(MP_QSTR_dot), (mp_obj_t)&linalg_dot_obj },
|
||||
{ MP_ROM_QSTR(MP_QSTR_zeros), (mp_obj_t)&linalg_zeros_obj },
|
||||
{ MP_ROM_QSTR(MP_QSTR_ones), (mp_obj_t)&linalg_ones_obj },
|
||||
{ MP_ROM_QSTR(MP_QSTR_eye), (mp_obj_t)&linalg_eye_obj },
|
||||
{ MP_ROM_QSTR(MP_QSTR_det), (mp_obj_t)&linalg_det_obj },
|
||||
{ MP_ROM_QSTR(MP_QSTR_eig), (mp_obj_t)&linalg_eig_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_acos), (mp_obj_t)&vectorise_acos_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_acosh), (mp_obj_t)&vectorise_acosh_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_asin), (mp_obj_t)&vectorise_asin_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_asinh), (mp_obj_t)&vectorise_asinh_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_atan), (mp_obj_t)&vectorise_atan_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_atanh), (mp_obj_t)&vectorise_atanh_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_ceil), (mp_obj_t)&vectorise_ceil_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_cos), (mp_obj_t)&vectorise_cos_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_erf), (mp_obj_t)&vectorise_erf_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_erfc), (mp_obj_t)&vectorise_erfc_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_exp), (mp_obj_t)&vectorise_exp_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_expm1), (mp_obj_t)&vectorise_expm1_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_floor), (mp_obj_t)&vectorise_floor_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_gamma), (mp_obj_t)&vectorise_gamma_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_lgamma), (mp_obj_t)&vectorise_lgamma_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_log), (mp_obj_t)&vectorise_log_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_log10), (mp_obj_t)&vectorise_log10_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_log2), (mp_obj_t)&vectorise_log2_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_sin), (mp_obj_t)&vectorise_sin_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_sinh), (mp_obj_t)&vectorise_sinh_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_sqrt), (mp_obj_t)&vectorise_sqrt_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_tan), (mp_obj_t)&vectorise_tan_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_tanh), (mp_obj_t)&vectorise_tanh_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_linspace), (mp_obj_t)&numerical_linspace_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_sum), (mp_obj_t)&numerical_sum_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_mean), (mp_obj_t)&numerical_mean_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_std), (mp_obj_t)&numerical_std_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_min), (mp_obj_t)&numerical_min_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_max), (mp_obj_t)&numerical_max_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_argmin), (mp_obj_t)&numerical_argmin_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_argmax), (mp_obj_t)&numerical_argmax_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_roll), (mp_obj_t)&numerical_roll_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_flip), (mp_obj_t)&numerical_flip_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_diff), (mp_obj_t)&numerical_diff_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_sort), (mp_obj_t)&numerical_sort_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_argsort), (mp_obj_t)&numerical_argsort_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_polyval), (mp_obj_t)&poly_polyval_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_polyfit), (mp_obj_t)&poly_polyfit_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_fft), (mp_obj_t)&fft_fft_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_ifft), (mp_obj_t)&fft_ifft_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_spectrum), (mp_obj_t)&fft_spectrum_obj },
|
||||
// class constants
|
||||
{ MP_ROM_QSTR(MP_QSTR_uint8), MP_ROM_INT(NDARRAY_UINT8) },
|
||||
{ MP_ROM_QSTR(MP_QSTR_int8), MP_ROM_INT(NDARRAY_INT8) },
|
||||
{ MP_ROM_QSTR(MP_QSTR_uint16), MP_ROM_INT(NDARRAY_UINT16) },
|
||||
{ MP_ROM_QSTR(MP_QSTR_int16), MP_ROM_INT(NDARRAY_INT16) },
|
||||
{ MP_ROM_QSTR(MP_QSTR_float), MP_ROM_INT(NDARRAY_FLOAT) },
|
||||
};
|
||||
|
||||
STATIC MP_DEFINE_CONST_DICT (
|
||||
mp_module_ulab_globals,
|
||||
ulab_globals_table
|
||||
);
|
||||
|
||||
const mp_obj_module_t ulab_user_cmodule = {
|
||||
.base = { &mp_type_module },
|
||||
.globals = (mp_obj_dict_t*)&mp_module_ulab_globals,
|
||||
};
|
||||
|
||||
MP_REGISTER_MODULE(MP_QSTR_ulab, ulab_user_cmodule, MODULE_ULAB_ENABLED);
|
||||
8
code/__init__.h
Normal file
8
code/__init__.h
Normal file
|
|
@ -0,0 +1,8 @@
|
|||
#ifndef MICROPY_INCLUDED_SHARED_BINDINGS_ULAB___INIT___H
|
||||
#define MICROPY_INCLUDED_SHARED_BINDINGS_ULAB___INIT___H
|
||||
|
||||
#include "py/obj.h"
|
||||
|
||||
// Nothing now.
|
||||
|
||||
#endif // MICROPY_INCLUDED_SHARED_BINDINGS_ULAB___INIT___H
|
||||
13
code/compat.h
Normal file
13
code/compat.h
Normal file
|
|
@ -0,0 +1,13 @@
|
|||
#ifndef MICROPY_INCLUDED_ULAB_COMPAT_H
|
||||
#define MICROPY_INCLUDED_ULAB_COMPAT_H
|
||||
|
||||
#pragma GCC diagnostic ignored "-Wshadow"
|
||||
|
||||
#define mp_obj_is_type(obj, type) MP_OBJ_IS_TYPE(obj, type)
|
||||
|
||||
#define MP_ROM_NONE (MP_ROM_PTR(&mp_const_none_obj))
|
||||
#define MP_ROM_FALSE (mp_const_false)
|
||||
#define MP_ROM_TRUE (mp_const_true)
|
||||
|
||||
#endif // MICROPY_INCLUDED_ULAB_COMPAT_H
|
||||
|
||||
|
|
@ -1,33 +0,0 @@
|
|||
|
||||
/*
|
||||
* This file is part of the micropython-ulab project,
|
||||
*
|
||||
* https://github.com/v923z/micropython-ulab
|
||||
*
|
||||
* The MIT License (MIT)
|
||||
*
|
||||
* Copyright (c) 2020 Zoltán Vörös
|
||||
*/
|
||||
|
||||
#include <math.h>
|
||||
#include <stdlib.h>
|
||||
#include <string.h>
|
||||
#include "py/obj.h"
|
||||
#include "py/runtime.h"
|
||||
#include "py/misc.h"
|
||||
#include "extras.h"
|
||||
|
||||
#if ULAB_EXTRAS_MODULE
|
||||
|
||||
STATIC const mp_rom_map_elem_t ulab_filter_globals_table[] = {
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR___name__), MP_OBJ_NEW_QSTR(MP_QSTR_extras) },
|
||||
};
|
||||
|
||||
STATIC MP_DEFINE_CONST_DICT(mp_module_ulab_extras_globals, ulab_extras_globals_table);
|
||||
|
||||
mp_obj_module_t ulab_filter_module = {
|
||||
.base = { &mp_type_module },
|
||||
.globals = (mp_obj_dict_t*)&mp_module_ulab_extras_globals,
|
||||
};
|
||||
|
||||
#endif
|
||||
|
|
@ -1,23 +0,0 @@
|
|||
|
||||
/*
|
||||
* This file is part of the micropython-ulab project,
|
||||
*
|
||||
* https://github.com/v923z/micropython-ulab
|
||||
*
|
||||
* The MIT License (MIT)
|
||||
*
|
||||
* Copyright (c) 2020 Zoltán Vörös
|
||||
*/
|
||||
|
||||
#ifndef _EXTRA_
|
||||
#define _EXTRA_
|
||||
|
||||
#include "ulab.h"
|
||||
#include "ndarray.h"
|
||||
|
||||
#if ULAB_EXTRAS_MODULE
|
||||
|
||||
mp_obj_module_t ulab_extras_module;
|
||||
|
||||
#endif
|
||||
#endif
|
||||
46
code/fft.c
46
code/fft.c
|
|
@ -1,4 +1,3 @@
|
|||
|
||||
/*
|
||||
* This file is part of the micropython-ulab project,
|
||||
*
|
||||
|
|
@ -8,21 +7,19 @@
|
|||
*
|
||||
* Copyright (c) 2019-2020 Zoltán Vörös
|
||||
*/
|
||||
|
||||
|
||||
#include <math.h>
|
||||
#include <stdio.h>
|
||||
#include <stdlib.h>
|
||||
#include <string.h>
|
||||
#include "py/runtime.h"
|
||||
#include "py/builtin.h"
|
||||
#include "py/binary.h"
|
||||
#include "py/obj.h"
|
||||
#include "py/objarray.h"
|
||||
#include "compat.h"
|
||||
#include "ndarray.h"
|
||||
#include "fft.h"
|
||||
|
||||
#if ULAB_FFT_MODULE
|
||||
|
||||
enum FFT_TYPE {
|
||||
FFT_FFT,
|
||||
FFT_IFFT,
|
||||
|
|
@ -103,9 +100,8 @@ mp_obj_t fft_fft_ifft_spectrum(size_t n_args, mp_obj_t arg_re, mp_obj_t arg_im,
|
|||
memcpy((mp_float_t *)out_re->array->items, (mp_float_t *)re->array->items, re->bytes);
|
||||
} else {
|
||||
for(size_t i=0; i < len; i++) {
|
||||
*data_re++ = ndarray_get_float_value(re->array->items, re->array->typecode, i);
|
||||
data_re[i] = ndarray_get_float_value(re->array->items, re->array->typecode, i);
|
||||
}
|
||||
data_re -= len;
|
||||
}
|
||||
ndarray_obj_t *out_im = create_new_ndarray(1, len, NDARRAY_FLOAT);
|
||||
mp_float_t *data_im = (mp_float_t *)out_im->array->items;
|
||||
|
|
@ -119,27 +115,23 @@ mp_obj_t fft_fft_ifft_spectrum(size_t n_args, mp_obj_t arg_re, mp_obj_t arg_im,
|
|||
memcpy((mp_float_t *)out_im->array->items, (mp_float_t *)im->array->items, im->bytes);
|
||||
} else {
|
||||
for(size_t i=0; i < len; i++) {
|
||||
*data_im++ = ndarray_get_float_value(im->array->items, im->array->typecode, i);
|
||||
data_im[i] = ndarray_get_float_value(im->array->items, im->array->typecode, i);
|
||||
}
|
||||
data_im -= len;
|
||||
}
|
||||
}
|
||||
|
||||
if((type == FFT_FFT) || (type == FFT_SPECTRUM)) {
|
||||
fft_kernel(data_re, data_im, len, 1);
|
||||
if(type == FFT_SPECTRUM) {
|
||||
for(size_t i=0; i < len; i++) {
|
||||
*data_re = MICROPY_FLOAT_C_FUN(sqrt)(*data_re * *data_re + *data_im * *data_im);
|
||||
data_re++;
|
||||
data_im++;
|
||||
data_re[i] = MICROPY_FLOAT_C_FUN(sqrt)(data_re[i]*data_re[i] + data_im[i]*data_im[i]);
|
||||
}
|
||||
}
|
||||
} else { // inverse transform
|
||||
fft_kernel(data_re, data_im, len, -1);
|
||||
// TODO: numpy accepts the norm keyword argument
|
||||
for(size_t i=0; i < len; i++) {
|
||||
*data_re++ /= len;
|
||||
*data_im++ /= len;
|
||||
data_re[i] /= len;
|
||||
data_im[i] /= len;
|
||||
}
|
||||
}
|
||||
if(type == FFT_SPECTRUM) {
|
||||
|
|
@ -160,8 +152,6 @@ mp_obj_t fft_fft(size_t n_args, const mp_obj_t *args) {
|
|||
}
|
||||
}
|
||||
|
||||
MP_DEFINE_CONST_FUN_OBJ_VAR_BETWEEN(fft_fft_obj, 1, 2, fft_fft);
|
||||
|
||||
mp_obj_t fft_ifft(size_t n_args, const mp_obj_t *args) {
|
||||
if(n_args == 2) {
|
||||
return fft_fft_ifft_spectrum(n_args, args[0], args[1], FFT_IFFT);
|
||||
|
|
@ -170,8 +160,6 @@ mp_obj_t fft_ifft(size_t n_args, const mp_obj_t *args) {
|
|||
}
|
||||
}
|
||||
|
||||
MP_DEFINE_CONST_FUN_OBJ_VAR_BETWEEN(fft_ifft_obj, 1, 2, fft_ifft);
|
||||
|
||||
mp_obj_t fft_spectrum(size_t n_args, const mp_obj_t *args) {
|
||||
if(n_args == 2) {
|
||||
return fft_fft_ifft_spectrum(n_args, args[0], args[1], FFT_SPECTRUM);
|
||||
|
|
@ -179,23 +167,3 @@ mp_obj_t fft_spectrum(size_t n_args, const mp_obj_t *args) {
|
|||
return fft_fft_ifft_spectrum(n_args, args[0], mp_const_none, FFT_SPECTRUM);
|
||||
}
|
||||
}
|
||||
|
||||
MP_DEFINE_CONST_FUN_OBJ_VAR_BETWEEN(fft_spectrum_obj, 1, 2, fft_spectrum);
|
||||
|
||||
#if !CIRCUITPY
|
||||
STATIC const mp_rom_map_elem_t ulab_fft_globals_table[] = {
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR___name__), MP_OBJ_NEW_QSTR(MP_QSTR_fft) },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_fft), (mp_obj_t)&fft_fft_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_ifft), (mp_obj_t)&fft_ifft_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_spectrum), (mp_obj_t)&fft_spectrum_obj },
|
||||
};
|
||||
|
||||
STATIC MP_DEFINE_CONST_DICT(mp_module_ulab_fft_globals, ulab_fft_globals_table);
|
||||
|
||||
mp_obj_module_t ulab_fft_module = {
|
||||
.base = { &mp_type_module },
|
||||
.globals = (mp_obj_dict_t*)&mp_module_ulab_fft_globals,
|
||||
};
|
||||
#endif
|
||||
|
||||
#endif
|
||||
|
|
|
|||
18
code/fft.h
18
code/fft.h
|
|
@ -1,4 +1,3 @@
|
|||
|
||||
/*
|
||||
* This file is part of the micropython-ulab project,
|
||||
*
|
||||
|
|
@ -6,12 +5,11 @@
|
|||
*
|
||||
* The MIT License (MIT)
|
||||
*
|
||||
* Copyright (c) 2019-2020 Zoltán Vörös
|
||||
* Copyright (c) 2019 Zoltán Vörös
|
||||
*/
|
||||
|
||||
|
||||
#ifndef _FFT_
|
||||
#define _FFT_
|
||||
#include "ulab.h"
|
||||
|
||||
#ifndef MP_PI
|
||||
#define MP_PI MICROPY_FLOAT_CONST(3.14159265358979323846)
|
||||
|
|
@ -19,13 +17,7 @@
|
|||
|
||||
#define SWAP(t, a, b) { t tmp = a; a = b; b = tmp; }
|
||||
|
||||
#if ULAB_FFT_MODULE
|
||||
|
||||
extern mp_obj_module_t ulab_fft_module;
|
||||
|
||||
MP_DECLARE_CONST_FUN_OBJ_VAR_BETWEEN(fft_fft_obj);
|
||||
MP_DECLARE_CONST_FUN_OBJ_VAR_BETWEEN(fft_ifft_obj);
|
||||
MP_DECLARE_CONST_FUN_OBJ_VAR_BETWEEN(fft_spectrum_obj);
|
||||
|
||||
#endif
|
||||
mp_obj_t fft_fft(size_t , const mp_obj_t *);
|
||||
mp_obj_t fft_ifft(size_t , const mp_obj_t *);
|
||||
mp_obj_t fft_spectrum(size_t , const mp_obj_t *);
|
||||
#endif
|
||||
|
|
|
|||
101
code/filter.c
101
code/filter.c
|
|
@ -1,101 +0,0 @@
|
|||
|
||||
/*
|
||||
* This file is part of the micropython-ulab project,
|
||||
*
|
||||
* https://github.com/v923z/micropython-ulab
|
||||
*
|
||||
* The MIT License (MIT)
|
||||
*
|
||||
* Copyright (c) 2020 Jeff Epler for Adafruit Industries
|
||||
*/
|
||||
|
||||
#include <math.h>
|
||||
#include <stdlib.h>
|
||||
#include <string.h>
|
||||
#include "py/obj.h"
|
||||
#include "py/runtime.h"
|
||||
#include "py/misc.h"
|
||||
#include "filter.h"
|
||||
|
||||
#if ULAB_FILTER_MODULE
|
||||
mp_obj_t filter_convolve(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
|
||||
static const mp_arg_t allowed_args[] = {
|
||||
{ MP_QSTR_a, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = mp_const_none } },
|
||||
{ MP_QSTR_v, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = mp_const_none } },
|
||||
};
|
||||
|
||||
mp_arg_val_t args[MP_ARRAY_SIZE(allowed_args)];
|
||||
mp_arg_parse_all(2, pos_args, kw_args, MP_ARRAY_SIZE(allowed_args), allowed_args, args);
|
||||
|
||||
if(!MP_OBJ_IS_TYPE(args[0].u_obj, &ulab_ndarray_type) || !MP_OBJ_IS_TYPE(args[1].u_obj, &ulab_ndarray_type)) {
|
||||
mp_raise_TypeError(translate("convolve arguments must be ndarrays"));
|
||||
}
|
||||
|
||||
ndarray_obj_t *a = MP_OBJ_TO_PTR(args[0].u_obj);
|
||||
ndarray_obj_t *c = MP_OBJ_TO_PTR(args[1].u_obj);
|
||||
int len_a = a->array->len;
|
||||
int len_c = c->array->len;
|
||||
// deal with linear arrays only
|
||||
if(a->m*a->n != len_a || c->m*c->n != len_c) {
|
||||
mp_raise_TypeError(translate("convolve arguments must be linear arrays"));
|
||||
}
|
||||
if(len_a == 0 || len_c == 0) {
|
||||
mp_raise_TypeError(translate("convolve arguments must not be empty"));
|
||||
}
|
||||
|
||||
int len = len_a + len_c - 1; // convolve mode "full"
|
||||
ndarray_obj_t *out = create_new_ndarray(1, len, NDARRAY_FLOAT);
|
||||
mp_float_t *outptr = out->array->items;
|
||||
int off = len_c-1;
|
||||
|
||||
if(a->array->typecode == NDARRAY_FLOAT && c->array->typecode == NDARRAY_FLOAT) {
|
||||
mp_float_t* a_items = (mp_float_t*)a->array->items;
|
||||
mp_float_t* c_items = (mp_float_t*)c->array->items;
|
||||
for(int k=-off; k<len-off; k++) {
|
||||
mp_float_t accum = (mp_float_t)0;
|
||||
int top_n = MIN(len_c, len_a - k);
|
||||
int bot_n = MAX(-k, 0);
|
||||
mp_float_t* a_ptr = a_items + bot_n + k;
|
||||
mp_float_t* a_end = a_ptr + (top_n - bot_n);
|
||||
mp_float_t* c_ptr = c_items + len_c - bot_n - 1;
|
||||
for(; a_ptr != a_end;) {
|
||||
accum += *a_ptr++ * *c_ptr--;
|
||||
}
|
||||
*outptr++ = accum;
|
||||
}
|
||||
} else {
|
||||
for(int k=-off; k<len-off; k++) {
|
||||
mp_float_t accum = (mp_float_t)0;
|
||||
int top_n = MIN(len_c, len_a - k);
|
||||
int bot_n = MAX(-k, 0);
|
||||
for(int n=bot_n; n<top_n; n++) {
|
||||
int idx_c = len_c - n - 1;
|
||||
int idx_a = n+k;
|
||||
mp_float_t ai = ndarray_get_float_value(a->array->items, a->array->typecode, idx_a);
|
||||
mp_float_t ci = ndarray_get_float_value(c->array->items, c->array->typecode, idx_c);
|
||||
accum += ai * ci;
|
||||
}
|
||||
*outptr++ = accum;
|
||||
}
|
||||
}
|
||||
|
||||
return out;
|
||||
}
|
||||
|
||||
MP_DEFINE_CONST_FUN_OBJ_KW(filter_convolve_obj, 2, filter_convolve);
|
||||
|
||||
#if !CIRCUITPY
|
||||
STATIC const mp_rom_map_elem_t ulab_filter_globals_table[] = {
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR___name__), MP_OBJ_NEW_QSTR(MP_QSTR_filter) },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_convolve), (mp_obj_t)&filter_convolve_obj },
|
||||
};
|
||||
|
||||
STATIC MP_DEFINE_CONST_DICT(mp_module_ulab_filter_globals, ulab_filter_globals_table);
|
||||
|
||||
mp_obj_module_t ulab_filter_module = {
|
||||
.base = { &mp_type_module },
|
||||
.globals = (mp_obj_dict_t*)&mp_module_ulab_filter_globals,
|
||||
};
|
||||
#endif
|
||||
|
||||
#endif
|
||||
|
|
@ -1,25 +0,0 @@
|
|||
|
||||
/*
|
||||
* This file is part of the micropython-ulab project,
|
||||
*
|
||||
* https://github.com/v923z/micropython-ulab
|
||||
*
|
||||
* The MIT License (MIT)
|
||||
*
|
||||
* Copyright (c) 2020 Jeff Epler for Adafruit Industries
|
||||
*/
|
||||
|
||||
#ifndef _FILTER_
|
||||
#define _FILTER_
|
||||
|
||||
#include "ulab.h"
|
||||
#include "ndarray.h"
|
||||
|
||||
#if ULAB_FILTER_MODULE
|
||||
|
||||
extern mp_obj_module_t ulab_filter_module;
|
||||
|
||||
MP_DECLARE_CONST_FUN_OBJ_KW(filter_convolve_obj);
|
||||
|
||||
#endif
|
||||
#endif
|
||||
120
code/linalg.c
120
code/linalg.c
|
|
@ -1,4 +1,3 @@
|
|||
|
||||
/*
|
||||
* This file is part of the micropython-ulab project,
|
||||
*
|
||||
|
|
@ -8,21 +7,74 @@
|
|||
*
|
||||
* Copyright (c) 2019-2020 Zoltán Vörös
|
||||
*/
|
||||
|
||||
|
||||
#include <stdlib.h>
|
||||
#include <string.h>
|
||||
#include <math.h>
|
||||
#include "py/obj.h"
|
||||
#include "py/runtime.h"
|
||||
#include "py/misc.h"
|
||||
#include "compat.h"
|
||||
#include "linalg.h"
|
||||
|
||||
#if ULAB_LINALG_MODULE
|
||||
mp_obj_t linalg_transpose(mp_obj_t self_in) {
|
||||
ndarray_obj_t *self = MP_OBJ_TO_PTR(self_in);
|
||||
// the size of a single item in the array
|
||||
uint8_t _sizeof = mp_binary_get_size('@', self->array->typecode, NULL);
|
||||
|
||||
// NOTE:
|
||||
// if the matrices are square, we can simply swap items, but
|
||||
// generic matrices can't be transposed in place, so we have to
|
||||
// declare a temporary variable
|
||||
|
||||
// NOTE:
|
||||
// In the old matrix, the coordinate (m, n) is m*self->n + n
|
||||
// We have to assign this to the coordinate (n, m) in the new
|
||||
// matrix, i.e., to n*self->m + m (since the new matrix has self->m columns)
|
||||
|
||||
// one-dimensional arrays can be transposed by simply swapping the dimensions
|
||||
if((self->m != 1) && (self->n != 1)) {
|
||||
uint8_t *c = (uint8_t *)self->array->items;
|
||||
// self->bytes is the size of the bytearray, irrespective of the typecode
|
||||
uint8_t *tmp = m_new(uint8_t, self->bytes);
|
||||
for(size_t m=0; m < self->m; m++) {
|
||||
for(size_t n=0; n < self->n; n++) {
|
||||
memcpy(tmp+_sizeof*(n*self->m + m), c+_sizeof*(m*self->n + n), _sizeof);
|
||||
}
|
||||
}
|
||||
memcpy(self->array->items, tmp, self->bytes);
|
||||
m_del(uint8_t, tmp, self->bytes);
|
||||
}
|
||||
SWAP(size_t, self->m, self->n);
|
||||
return mp_const_none;
|
||||
}
|
||||
|
||||
mp_obj_t linalg_reshape(mp_obj_t self_in, mp_obj_t shape) {
|
||||
ndarray_obj_t *self = MP_OBJ_TO_PTR(self_in);
|
||||
if(!MP_OBJ_IS_TYPE(shape, &mp_type_tuple) || (MP_OBJ_SMALL_INT_VALUE(mp_obj_len_maybe(shape)) != 2)) {
|
||||
mp_raise_ValueError(translate("shape must be a 2-tuple"));
|
||||
}
|
||||
|
||||
mp_obj_iter_buf_t iter_buf;
|
||||
mp_obj_t item, iterable = mp_getiter(shape, &iter_buf);
|
||||
uint16_t m, n;
|
||||
item = mp_iternext(iterable);
|
||||
m = mp_obj_get_int(item);
|
||||
item = mp_iternext(iterable);
|
||||
n = mp_obj_get_int(item);
|
||||
if(m*n != self->m*self->n) {
|
||||
// TODO: the proper error message would be "cannot reshape array of size %d into shape (%d, %d)"
|
||||
mp_raise_ValueError(translate("cannot reshape array (incompatible input/output shape)"));
|
||||
}
|
||||
self->m = m;
|
||||
self->n = n;
|
||||
return MP_OBJ_FROM_PTR(self);
|
||||
}
|
||||
|
||||
mp_obj_t linalg_size(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
|
||||
static const mp_arg_t allowed_args[] = {
|
||||
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = mp_const_none } },
|
||||
{ MP_QSTR_axis, MP_ARG_KW_ONLY | MP_ARG_OBJ, {.u_rom_obj = mp_const_none } },
|
||||
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_NONE } },
|
||||
{ MP_QSTR_axis, MP_ARG_KW_ONLY | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_NONE } },
|
||||
};
|
||||
|
||||
mp_arg_val_t args[MP_ARRAY_SIZE(allowed_args)];
|
||||
|
|
@ -49,7 +101,7 @@ mp_obj_t linalg_size(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args)
|
|||
return mp_obj_new_int(ndarray->n);
|
||||
}
|
||||
} else {
|
||||
mp_raise_ValueError(translate("tuple index out of range"));
|
||||
mp_raise_ValueError(translate("tuple index out of range"));
|
||||
}
|
||||
} else {
|
||||
mp_raise_TypeError(translate("wrong argument type"));
|
||||
|
|
@ -57,8 +109,6 @@ mp_obj_t linalg_size(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args)
|
|||
}
|
||||
}
|
||||
|
||||
MP_DEFINE_CONST_FUN_OBJ_KW(linalg_size_obj, 1, linalg_size);
|
||||
|
||||
bool linalg_invert_matrix(mp_float_t *data, size_t N) {
|
||||
// returns true, of the inversion was successful,
|
||||
// false, if the matrix is singular
|
||||
|
|
@ -134,13 +184,8 @@ mp_obj_t linalg_inv(mp_obj_t o_in) {
|
|||
return MP_OBJ_FROM_PTR(inverted);
|
||||
}
|
||||
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(linalg_inv_obj, linalg_inv);
|
||||
|
||||
mp_obj_t linalg_dot(mp_obj_t _m1, mp_obj_t _m2) {
|
||||
// TODO: should the results be upcast?
|
||||
if(!MP_OBJ_IS_TYPE(_m1, &ulab_ndarray_type) || !MP_OBJ_IS_TYPE(_m2, &ulab_ndarray_type)) {
|
||||
mp_raise_TypeError(translate("arguments must be ndarrays"));
|
||||
}
|
||||
ndarray_obj_t *m1 = MP_OBJ_TO_PTR(_m1);
|
||||
ndarray_obj_t *m2 = MP_OBJ_TO_PTR(_m2);
|
||||
if(m1->n != m2->m) {
|
||||
|
|
@ -165,8 +210,6 @@ mp_obj_t linalg_dot(mp_obj_t _m1, mp_obj_t _m2) {
|
|||
return MP_OBJ_FROM_PTR(out);
|
||||
}
|
||||
|
||||
MP_DEFINE_CONST_FUN_OBJ_2(linalg_dot_obj, linalg_dot);
|
||||
|
||||
mp_obj_t linalg_zeros_ones(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args, uint8_t kind) {
|
||||
static const mp_arg_t allowed_args[] = {
|
||||
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_obj = MP_OBJ_NULL} } ,
|
||||
|
|
@ -187,7 +230,7 @@ mp_obj_t linalg_zeros_ones(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw
|
|||
} else if(MP_OBJ_IS_TYPE(args[0].u_obj, &mp_type_tuple)) {
|
||||
mp_obj_tuple_t *tuple = MP_OBJ_TO_PTR(args[0].u_obj);
|
||||
if(tuple->len != 2) {
|
||||
mp_raise_TypeError(translate("input argument must be an integer or a 2-tuple"));
|
||||
mp_raise_TypeError(translate("input argument must be an integer or a 2-tuple"));
|
||||
}
|
||||
ndarray = create_new_ndarray(mp_obj_get_int(tuple->items[0]),
|
||||
mp_obj_get_int(tuple->items[1]), dtype);
|
||||
|
|
@ -205,18 +248,14 @@ mp_obj_t linalg_zeros(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args
|
|||
return linalg_zeros_ones(n_args, pos_args, kw_args, 0);
|
||||
}
|
||||
|
||||
MP_DEFINE_CONST_FUN_OBJ_KW(linalg_zeros_obj, 0, linalg_zeros);
|
||||
|
||||
mp_obj_t linalg_ones(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
|
||||
return linalg_zeros_ones(n_args, pos_args, kw_args, 1);
|
||||
}
|
||||
|
||||
MP_DEFINE_CONST_FUN_OBJ_KW(linalg_ones_obj, 0, linalg_ones);
|
||||
|
||||
mp_obj_t linalg_eye(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
|
||||
static const mp_arg_t allowed_args[] = {
|
||||
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_INT, {.u_int = 0} },
|
||||
{ MP_QSTR_M, MP_ARG_KW_ONLY | MP_ARG_OBJ, {.u_rom_obj = mp_const_none } },
|
||||
{ MP_QSTR_M, MP_ARG_KW_ONLY | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_NONE } },
|
||||
{ MP_QSTR_k, MP_ARG_KW_ONLY | MP_ARG_INT, {.u_int = 0} },
|
||||
{ MP_QSTR_dtype, MP_ARG_KW_ONLY | MP_ARG_INT, {.u_int = NDARRAY_FLOAT} },
|
||||
};
|
||||
|
|
@ -236,16 +275,16 @@ mp_obj_t linalg_eye(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args)
|
|||
ndarray_obj_t *ndarray = create_new_ndarray(m, n, dtype);
|
||||
mp_obj_t one = mp_obj_new_int(1);
|
||||
size_t i = 0;
|
||||
if((k >= 0) && (k < n)) {
|
||||
while(k < n) {
|
||||
if((k >= 0) && (abs(k) < n)) {
|
||||
while(abs(k) < n) {
|
||||
mp_binary_set_val_array(dtype, ndarray->array->items, i*n+k, one);
|
||||
k++;
|
||||
i++;
|
||||
}
|
||||
} else if((k < 0) && (-k < m)) {
|
||||
} else if((k < 0) && (abs(k) < m)) {
|
||||
k = -k;
|
||||
i = 0;
|
||||
while(k < m) {
|
||||
while(abs(k) < m) {
|
||||
mp_binary_set_val_array(dtype, ndarray->array->items, k*n+i, one);
|
||||
k++;
|
||||
i++;
|
||||
|
|
@ -254,8 +293,6 @@ mp_obj_t linalg_eye(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args)
|
|||
return MP_OBJ_FROM_PTR(ndarray);
|
||||
}
|
||||
|
||||
MP_DEFINE_CONST_FUN_OBJ_KW(linalg_eye_obj, 0, linalg_eye);
|
||||
|
||||
mp_obj_t linalg_det(mp_obj_t oin) {
|
||||
if(!MP_OBJ_IS_TYPE(oin, &ulab_ndarray_type)) {
|
||||
mp_raise_TypeError(translate("function defined for ndarrays only"));
|
||||
|
|
@ -293,8 +330,6 @@ mp_obj_t linalg_det(mp_obj_t oin) {
|
|||
return mp_obj_new_float(det);
|
||||
}
|
||||
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(linalg_det_obj, linalg_det);
|
||||
|
||||
mp_obj_t linalg_eig(mp_obj_t oin) {
|
||||
if(!MP_OBJ_IS_TYPE(oin, &ulab_ndarray_type)) {
|
||||
mp_raise_TypeError(translate("function defined for ndarrays only"));
|
||||
|
|
@ -406,7 +441,7 @@ mp_obj_t linalg_eig(mp_obj_t oin) {
|
|||
if(iterations == 0) {
|
||||
// the computation did not converge; numpy raises LinAlgError
|
||||
m_del(mp_float_t, array, in->array->len);
|
||||
mp_raise_ValueError(translate("iterations did not converge"));
|
||||
mp_raise_ValueError(translate("iterations did not converge")) ;
|
||||
}
|
||||
ndarray_obj_t *eigenvalues = create_new_ndarray(1, in->n, NDARRAY_FLOAT);
|
||||
mp_float_t *eigvalues = (mp_float_t *)eigenvalues->array->items;
|
||||
|
|
@ -421,28 +456,3 @@ mp_obj_t linalg_eig(mp_obj_t oin) {
|
|||
return tuple;
|
||||
return MP_OBJ_FROM_PTR(eigenvalues);
|
||||
}
|
||||
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(linalg_eig_obj, linalg_eig);
|
||||
|
||||
#if !CIRCUITPY
|
||||
STATIC const mp_rom_map_elem_t ulab_linalg_globals_table[] = {
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR___name__), MP_OBJ_NEW_QSTR(MP_QSTR_linalg) },
|
||||
{ MP_ROM_QSTR(MP_QSTR_size), (mp_obj_t)&linalg_size_obj },
|
||||
{ MP_ROM_QSTR(MP_QSTR_inv), (mp_obj_t)&linalg_inv_obj },
|
||||
{ MP_ROM_QSTR(MP_QSTR_dot), (mp_obj_t)&linalg_dot_obj },
|
||||
{ MP_ROM_QSTR(MP_QSTR_zeros), (mp_obj_t)&linalg_zeros_obj },
|
||||
{ MP_ROM_QSTR(MP_QSTR_ones), (mp_obj_t)&linalg_ones_obj },
|
||||
{ MP_ROM_QSTR(MP_QSTR_eye), (mp_obj_t)&linalg_eye_obj },
|
||||
{ MP_ROM_QSTR(MP_QSTR_det), (mp_obj_t)&linalg_det_obj },
|
||||
{ MP_ROM_QSTR(MP_QSTR_eig), (mp_obj_t)&linalg_eig_obj },
|
||||
};
|
||||
|
||||
STATIC MP_DEFINE_CONST_DICT(mp_module_ulab_linalg_globals, ulab_linalg_globals_table);
|
||||
|
||||
mp_obj_module_t ulab_linalg_module = {
|
||||
.base = { &mp_type_module },
|
||||
.globals = (mp_obj_dict_t*)&mp_module_ulab_linalg_globals,
|
||||
};
|
||||
#endif
|
||||
|
||||
#endif
|
||||
|
|
|
|||
|
|
@ -1,4 +1,3 @@
|
|||
|
||||
/*
|
||||
* This file is part of the micropython-ulab project,
|
||||
*
|
||||
|
|
@ -6,15 +5,16 @@
|
|||
*
|
||||
* The MIT License (MIT)
|
||||
*
|
||||
* Copyright (c) 2019-2020 Zoltán Vörös
|
||||
* Copyright (c) 2019 Zoltán Vörös
|
||||
*/
|
||||
|
||||
|
||||
#ifndef _LINALG_
|
||||
#define _LINALG_
|
||||
|
||||
#include "ulab.h"
|
||||
#include "ndarray.h"
|
||||
|
||||
#define SWAP(t, a, b) { t tmp = a; a = b; b = tmp; }
|
||||
|
||||
#if MICROPY_FLOAT_IMPL == MICROPY_FLOAT_IMPL_FLOAT
|
||||
#define epsilon 1.2e-7
|
||||
#elif MICROPY_FLOAT_IMPL == MICROPY_FLOAT_IMPL_DOUBLE
|
||||
|
|
@ -23,13 +23,17 @@
|
|||
|
||||
#define JACOBI_MAX 20
|
||||
|
||||
#if ULAB_LINALG_MODULE || ULAB_POLY_MODULE
|
||||
mp_obj_t linalg_transpose(mp_obj_t );
|
||||
mp_obj_t linalg_reshape(mp_obj_t , mp_obj_t );
|
||||
mp_obj_t linalg_size(size_t , const mp_obj_t *, mp_map_t *);
|
||||
bool linalg_invert_matrix(mp_float_t *, size_t );
|
||||
#endif
|
||||
mp_obj_t linalg_inv(mp_obj_t );
|
||||
mp_obj_t linalg_dot(mp_obj_t , mp_obj_t );
|
||||
mp_obj_t linalg_zeros(size_t , const mp_obj_t *, mp_map_t *);
|
||||
mp_obj_t linalg_ones(size_t , const mp_obj_t *, mp_map_t *);
|
||||
mp_obj_t linalg_eye(size_t , const mp_obj_t *, mp_map_t *);
|
||||
|
||||
#if ULAB_LINALG_MODULE
|
||||
|
||||
extern mp_obj_module_t ulab_linalg_module;
|
||||
mp_obj_t linalg_det(mp_obj_t );
|
||||
mp_obj_t linalg_eig(mp_obj_t );
|
||||
|
||||
#endif
|
||||
#endif
|
||||
|
|
|
|||
|
|
@ -8,12 +8,8 @@ SRC_USERMOD += $(USERMODULES_DIR)/vectorise.c
|
|||
SRC_USERMOD += $(USERMODULES_DIR)/poly.c
|
||||
SRC_USERMOD += $(USERMODULES_DIR)/fft.c
|
||||
SRC_USERMOD += $(USERMODULES_DIR)/numerical.c
|
||||
SRC_USERMOD += $(USERMODULES_DIR)/filter.c
|
||||
SRC_USERMOD += $(USERMODULES_DIR)/extras.c
|
||||
SRC_USERMOD += $(USERMODULES_DIR)/ulab.c
|
||||
|
||||
# We can add our module folder to include paths if needed
|
||||
# This is not actually needed in this example.
|
||||
CFLAGS_USERMOD += -I$(USERMODULES_DIR)
|
||||
|
||||
CFLAGS_EXTRA = -DMODULE_ULAB_ENABLED=1
|
||||
|
|
|
|||
209
code/ndarray.c
209
code/ndarray.c
|
|
@ -1,4 +1,3 @@
|
|||
|
||||
/*
|
||||
* This file is part of the micropython-ulab project,
|
||||
*
|
||||
|
|
@ -8,7 +7,7 @@
|
|||
*
|
||||
* Copyright (c) 2019-2020 Zoltán Vörös
|
||||
*/
|
||||
|
||||
|
||||
#include <math.h>
|
||||
#include <stdio.h>
|
||||
#include <stdlib.h>
|
||||
|
|
@ -17,6 +16,7 @@
|
|||
#include "py/binary.h"
|
||||
#include "py/obj.h"
|
||||
#include "py/objtuple.h"
|
||||
#include "compat.h"
|
||||
#include "ndarray.h"
|
||||
|
||||
// This function is copied verbatim from objarray.c
|
||||
|
|
@ -64,16 +64,15 @@ void fill_array_iterable(mp_float_t *array, mp_obj_t iterable) {
|
|||
|
||||
void ndarray_print_row(const mp_print_t *print, mp_obj_array_t *data, size_t n0, size_t n) {
|
||||
mp_print_str(print, "[");
|
||||
size_t i;
|
||||
if(n < PRINT_MAX) { // if the array is short, print everything
|
||||
mp_obj_print_helper(print, mp_binary_get_val_array(data->typecode, data->items, n0), PRINT_REPR);
|
||||
for(i=1; i<n; i++) {
|
||||
for(size_t i=1; i<n; i++) {
|
||||
mp_print_str(print, ", ");
|
||||
mp_obj_print_helper(print, mp_binary_get_val_array(data->typecode, data->items, n0+i), PRINT_REPR);
|
||||
}
|
||||
} else {
|
||||
mp_obj_print_helper(print, mp_binary_get_val_array(data->typecode, data->items, n0), PRINT_REPR);
|
||||
for(i=1; i<3; i++) {
|
||||
for(size_t i=1; i<3; i++) {
|
||||
mp_print_str(print, ", ");
|
||||
mp_obj_print_helper(print, mp_binary_get_val_array(data->typecode, data->items, n0+i), PRINT_REPR);
|
||||
}
|
||||
|
|
@ -154,7 +153,7 @@ mp_obj_t ndarray_copy(mp_obj_t self_in) {
|
|||
|
||||
STATIC uint8_t ndarray_init_helper(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
|
||||
static const mp_arg_t allowed_args[] = {
|
||||
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = mp_const_none } },
|
||||
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_NONE } },
|
||||
{ MP_QSTR_dtype, MP_ARG_KW_ONLY | MP_ARG_INT, {.u_int = NDARRAY_FLOAT } },
|
||||
};
|
||||
|
||||
|
|
@ -165,7 +164,13 @@ STATIC uint8_t ndarray_init_helper(size_t n_args, const mp_obj_t *pos_args, mp_m
|
|||
return dtype;
|
||||
}
|
||||
|
||||
STATIC mp_obj_t ndarray_make_new_core(const mp_obj_type_t *type, size_t n_args, size_t n_kw, const mp_obj_t *args, mp_map_t *kw_args) {
|
||||
mp_obj_t ndarray_make_new(const mp_obj_type_t *type, size_t n_args, const mp_obj_t *args, mp_map_t *kw_args) {
|
||||
mp_arg_check_num(n_args, kw_args, 1, 2, true);
|
||||
size_t n_kw = 0;
|
||||
if (kw_args != 0) {
|
||||
n_kw = kw_args->used;
|
||||
}
|
||||
mp_map_init_fixed_table(kw_args, n_kw, args + n_args);
|
||||
uint8_t dtype = ndarray_init_helper(n_args, args, kw_args);
|
||||
|
||||
size_t len1, len2=0, i=0;
|
||||
|
|
@ -186,7 +191,8 @@ STATIC mp_obj_t ndarray_make_new_core(const mp_obj_type_t *type, size_t n_args,
|
|||
if(len_in != MP_OBJ_NULL) { // indeed, this seems to be an iterable
|
||||
// Next, we have to check, whether all elements in the outer loop have the same length
|
||||
if(i > 0) {
|
||||
if(len2 != MP_OBJ_SMALL_INT_VALUE(len_in)) {
|
||||
size_t temp = abs(MP_OBJ_SMALL_INT_VALUE(len_in));
|
||||
if(len2 != temp) {
|
||||
mp_raise_ValueError(translate("iterables are not of the same length"));
|
||||
}
|
||||
}
|
||||
|
|
@ -212,31 +218,14 @@ STATIC mp_obj_t ndarray_make_new_core(const mp_obj_type_t *type, size_t n_args,
|
|||
return MP_OBJ_FROM_PTR(self);
|
||||
}
|
||||
|
||||
#ifdef CIRCUITPY
|
||||
mp_obj_t ndarray_make_new(const mp_obj_type_t *type, size_t n_args, const mp_obj_t *args, mp_map_t *kw_args) {
|
||||
mp_arg_check_num(n_args, kw_args, 1, 2, true);
|
||||
size_t n_kw = 0;
|
||||
if (kw_args != 0) {
|
||||
n_kw = kw_args->used;
|
||||
}
|
||||
mp_map_init_fixed_table(kw_args, n_kw, args + n_args);
|
||||
return ndarray_make_new_core(type, n_args, n_kw, args, kw_args);
|
||||
}
|
||||
#else
|
||||
mp_obj_t ndarray_make_new(const mp_obj_type_t *type, size_t n_args, size_t n_kw, const mp_obj_t *args) {
|
||||
mp_arg_check_num(n_args, n_kw, 1, 2, true);
|
||||
mp_map_t kw_args;
|
||||
mp_map_init_fixed_table(&kw_args, n_kw, args + n_args);
|
||||
return ndarray_make_new_core(type, n_args, n_kw, args, &kw_args);
|
||||
}
|
||||
#endif
|
||||
|
||||
size_t slice_length(mp_bound_slice_t slice) {
|
||||
int32_t len, correction = 1;
|
||||
if(slice.step > 0) correction = -1;
|
||||
len = (slice.stop - slice.start + (slice.step + correction)) / slice.step;
|
||||
if(len < 0) return 0;
|
||||
return (size_t)len;
|
||||
// TODO: check, whether this is true!
|
||||
if(slice.step < 0) {
|
||||
slice.step = -slice.step;
|
||||
return (slice.start - slice.stop) / slice.step;
|
||||
} else {
|
||||
return (slice.stop - slice.start) / slice.step;
|
||||
}
|
||||
}
|
||||
|
||||
size_t true_length(mp_obj_t bool_list) {
|
||||
|
|
@ -261,10 +250,10 @@ size_t true_length(mp_obj_t bool_list) {
|
|||
mp_bound_slice_t generate_slice(mp_uint_t n, mp_obj_t index) {
|
||||
// micropython seems to have difficulties with negative steps
|
||||
mp_bound_slice_t slice;
|
||||
if(MP_OBJ_IS_TYPE(index, &mp_type_slice)) {
|
||||
if(mp_obj_is_type(index, &mp_type_slice)) {
|
||||
mp_seq_get_fast_slice_indexes(n, index, &slice);
|
||||
} else if(MP_OBJ_IS_INT(index)) {
|
||||
int32_t _index = mp_obj_get_int(index);
|
||||
uint32_t _index = mp_obj_get_int(index);
|
||||
if(_index < 0) {
|
||||
_index += n;
|
||||
}
|
||||
|
|
@ -304,7 +293,7 @@ mp_obj_t insert_slice_list(ndarray_obj_t *ndarray, size_t m, size_t n,
|
|||
mp_obj_t row_list, mp_obj_t column_list,
|
||||
ndarray_obj_t *values) {
|
||||
if((m != values->m) && (n != values->n)) {
|
||||
if(values->array->len != 1) { // not a single item
|
||||
if((values->array->len != 1)) { // not a single item
|
||||
mp_raise_ValueError(translate("could not broadast input array from shape"));
|
||||
}
|
||||
}
|
||||
|
|
@ -367,7 +356,8 @@ mp_obj_t insert_slice_list(ndarray_obj_t *ndarray, size_t m, size_t n,
|
|||
} else { // columns are indexed by a list
|
||||
mp_obj_iter_buf_t column_iter_buf;
|
||||
mp_obj_t column_item, column_iterable;
|
||||
size_t j = 0, cindex = 0;
|
||||
size_t j = 0;
|
||||
cindex = 0;
|
||||
while((row_item = mp_iternext(row_iterable)) != MP_OBJ_STOP_ITERATION) {
|
||||
if(mp_obj_is_true(row_item)) {
|
||||
column_iterable = mp_getiter(column_list, &column_iter_buf);
|
||||
|
|
@ -453,7 +443,8 @@ mp_obj_t iterate_slice_list(ndarray_obj_t *ndarray, size_t m, size_t n,
|
|||
} else { // columns are indexed by a list
|
||||
mp_obj_iter_buf_t column_iter_buf;
|
||||
mp_obj_t column_item, column_iterable;
|
||||
size_t j = 0, cindex = 0;
|
||||
size_t j = 0;
|
||||
cindex = 0;
|
||||
while((row_item = mp_iternext(row_iterable)) != MP_OBJ_STOP_ITERATION) {
|
||||
if(mp_obj_is_true(row_item)) {
|
||||
column_iterable = mp_getiter(column_list, &column_iter_buf);
|
||||
|
|
@ -497,7 +488,7 @@ mp_obj_t ndarray_get_slice(ndarray_obj_t *ndarray, mp_obj_t index, ndarray_obj_t
|
|||
m = slice_length(row_slice);
|
||||
n = slice_length(column_slice);
|
||||
return iterate_slice_list(ndarray, m, n, row_slice, column_slice, mp_const_none, mp_const_none, values);
|
||||
} else if(MP_OBJ_IS_TYPE(index, &mp_type_list)) {
|
||||
} else if(mp_obj_is_type(index, &mp_type_list)) {
|
||||
n = true_length(index);
|
||||
if(ndarray->m == 1) { // we have a flat array
|
||||
// we might have to separate the n == 1 case
|
||||
|
|
@ -520,9 +511,9 @@ mp_obj_t ndarray_get_slice(ndarray_obj_t *ndarray, mp_obj_t index, ndarray_obj_t
|
|||
MP_OBJ_IS_INT(tuple->items[1]))) {
|
||||
mp_raise_msg(&mp_type_IndexError, translate("indices must be integers, slices, or Boolean lists"));
|
||||
}
|
||||
if(MP_OBJ_IS_TYPE(tuple->items[0], &mp_type_list)) { // rows are indexed by Boolean list
|
||||
if(mp_obj_is_type(tuple->items[0], &mp_type_list)) { // rows are indexed by Boolean list
|
||||
m = true_length(tuple->items[0]);
|
||||
if(MP_OBJ_IS_TYPE(tuple->items[1], &mp_type_list)) {
|
||||
if(mp_obj_is_type(tuple->items[1], &mp_type_list)) {
|
||||
n = true_length(tuple->items[1]);
|
||||
return iterate_slice_list(ndarray, m, n, row_slice, column_slice,
|
||||
tuple->items[0], tuple->items[1], values);
|
||||
|
|
@ -536,7 +527,7 @@ mp_obj_t ndarray_get_slice(ndarray_obj_t *ndarray, mp_obj_t index, ndarray_obj_t
|
|||
} else { // rows are indexed by a slice, or an integer
|
||||
row_slice = generate_slice(ndarray->m, tuple->items[0]);
|
||||
m = slice_length(row_slice);
|
||||
if(MP_OBJ_IS_TYPE(tuple->items[1], &mp_type_list)) { // columns are indexed by a Boolean list
|
||||
if(mp_obj_is_type(tuple->items[1], &mp_type_list)) { // columns are indexed by a Boolean list
|
||||
n = true_length(tuple->items[1]);
|
||||
return iterate_slice_list(ndarray, m, n, row_slice, column_slice,
|
||||
mp_const_none, tuple->items[1], values);
|
||||
|
|
@ -595,7 +586,7 @@ mp_obj_t ndarray_iternext(mp_obj_t self_in) {
|
|||
ndarray_obj_t *ndarray = MP_OBJ_TO_PTR(self->ndarray);
|
||||
// TODO: in numpy, ndarrays are iterated with respect to the first axis.
|
||||
size_t iter_end = 0;
|
||||
if(ndarray->m == 1) {
|
||||
if((ndarray->m == 1)) {
|
||||
iter_end = ndarray->array->len;
|
||||
} else {
|
||||
iter_end = ndarray->m;
|
||||
|
|
@ -640,14 +631,22 @@ mp_obj_t ndarray_shape(mp_obj_t self_in) {
|
|||
return mp_obj_new_tuple(2, tuple);
|
||||
}
|
||||
|
||||
mp_obj_t ndarray_size(mp_obj_t self_in) {
|
||||
mp_obj_t ndarray_rawsize(mp_obj_t self_in) {
|
||||
// returns a 5-tuple with the
|
||||
//
|
||||
// 0. number of rows
|
||||
// 1. number of columns
|
||||
// 2. length of the storage (should be equal to the product of 1. and 2.)
|
||||
// 3. length of the data storage in bytes
|
||||
// 4. datum size in bytes
|
||||
ndarray_obj_t *self = MP_OBJ_TO_PTR(self_in);
|
||||
return mp_obj_new_int(self->array->len);
|
||||
}
|
||||
|
||||
mp_obj_t ndarray_itemsize(mp_obj_t self_in) {
|
||||
ndarray_obj_t *self = MP_OBJ_TO_PTR(self_in);
|
||||
return MP_OBJ_NEW_SMALL_INT(mp_binary_get_size('@', self->array->typecode, NULL));
|
||||
mp_obj_tuple_t *tuple = MP_OBJ_TO_PTR(mp_obj_new_tuple(5, NULL));
|
||||
tuple->items[0] = MP_OBJ_NEW_SMALL_INT(self->m);
|
||||
tuple->items[1] = MP_OBJ_NEW_SMALL_INT(self->n);
|
||||
tuple->items[2] = MP_OBJ_NEW_SMALL_INT(self->array->len);
|
||||
tuple->items[3] = MP_OBJ_NEW_SMALL_INT(self->bytes);
|
||||
tuple->items[4] = MP_OBJ_NEW_SMALL_INT(mp_binary_get_size('@', self->array->typecode, NULL));
|
||||
return tuple;
|
||||
}
|
||||
|
||||
mp_obj_t ndarray_flatten(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
|
||||
|
|
@ -662,7 +661,7 @@ mp_obj_t ndarray_flatten(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_a
|
|||
|
||||
GET_STR_DATA_LEN(args[0].u_obj, order, len);
|
||||
if((len != 1) || ((memcmp(order, "C", 1) != 0) && (memcmp(order, "F", 1) != 0))) {
|
||||
mp_raise_ValueError(translate("flattening order must be either 'C', or 'F'"));
|
||||
mp_raise_ValueError(translate("flattening order must be either 'C', or 'F'"));
|
||||
}
|
||||
|
||||
// if order == 'C', we simply have to set m, and n, there is nothing else to do
|
||||
|
|
@ -685,6 +684,11 @@ mp_obj_t ndarray_flatten(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_a
|
|||
return self_copy;
|
||||
}
|
||||
|
||||
mp_obj_t ndarray_asbytearray(mp_obj_t self_in) {
|
||||
ndarray_obj_t *self = MP_OBJ_TO_PTR(self_in);
|
||||
return MP_OBJ_FROM_PTR(self->array);
|
||||
}
|
||||
|
||||
// Binary operations
|
||||
|
||||
mp_obj_t ndarray_binary_op(mp_binary_op_t op, mp_obj_t lhs, mp_obj_t rhs) {
|
||||
|
|
@ -840,6 +844,11 @@ mp_obj_t ndarray_binary_op(mp_binary_op_t op, mp_obj_t lhs, mp_obj_t rhs) {
|
|||
mp_obj_t ndarray_unary_op(mp_unary_op_t op, mp_obj_t self_in) {
|
||||
ndarray_obj_t *self = MP_OBJ_TO_PTR(self_in);
|
||||
ndarray_obj_t *ndarray = NULL;
|
||||
uint8_t *array8u;
|
||||
int8_t *array8i;
|
||||
uint16_t *array16u;
|
||||
int16_t *array16i;
|
||||
mp_float_t *arraymp;
|
||||
switch (op) {
|
||||
case MP_UNARY_OP_LEN:
|
||||
if(self->m > 1) {
|
||||
|
|
@ -856,28 +865,28 @@ mp_obj_t ndarray_unary_op(mp_unary_op_t op, mp_obj_t self_in) {
|
|||
// we can invert the content byte by byte, there is no need to distinguish
|
||||
// between different typecodes
|
||||
ndarray = MP_OBJ_TO_PTR(ndarray_copy(self_in));
|
||||
uint8_t *array = (uint8_t *)ndarray->array->items;
|
||||
for(size_t i=0; i < self->bytes; i++) array[i] = ~array[i];
|
||||
array8u = (uint8_t *)ndarray->array->items;
|
||||
for(size_t i=0; i < self->bytes; i++) array8u[i] = ~array8u[i];
|
||||
return MP_OBJ_FROM_PTR(ndarray);
|
||||
break;
|
||||
|
||||
case MP_UNARY_OP_NEGATIVE:
|
||||
ndarray = MP_OBJ_TO_PTR(ndarray_copy(self_in));
|
||||
if(self->array->typecode == NDARRAY_UINT8) {
|
||||
uint8_t *array = (uint8_t *)ndarray->array->items;
|
||||
for(size_t i=0; i < self->array->len; i++) array[i] = -array[i];
|
||||
array8u = (uint8_t *)ndarray->array->items;
|
||||
for(size_t i=0; i < self->array->len; i++) array8u[i] = -array8u[i];
|
||||
} else if(self->array->typecode == NDARRAY_INT8) {
|
||||
int8_t *array = (int8_t *)ndarray->array->items;
|
||||
for(size_t i=0; i < self->array->len; i++) array[i] = -array[i];
|
||||
array8i = (int8_t *)ndarray->array->items;
|
||||
for(size_t i=0; i < self->array->len; i++) array8i[i] = -array8i[i];
|
||||
} else if(self->array->typecode == NDARRAY_UINT16) {
|
||||
uint16_t *array = (uint16_t *)ndarray->array->items;
|
||||
for(size_t i=0; i < self->array->len; i++) array[i] = -array[i];
|
||||
array16u = (uint16_t *)ndarray->array->items;
|
||||
for(size_t i=0; i < self->array->len; i++) array16u[i] = -array16u[i];
|
||||
} else if(self->array->typecode == NDARRAY_INT16) {
|
||||
int16_t *array = (int16_t *)ndarray->array->items;
|
||||
for(size_t i=0; i < self->array->len; i++) array[i] = -array[i];
|
||||
array16i = (int16_t *)ndarray->array->items;
|
||||
for(size_t i=0; i < self->array->len; i++) array16i[i] = -array16i[i];
|
||||
} else {
|
||||
mp_float_t *array = (mp_float_t *)ndarray->array->items;
|
||||
for(size_t i=0; i < self->array->len; i++) array[i] = -array[i];
|
||||
arraymp = (mp_float_t *)ndarray->array->items;
|
||||
for(size_t i=0; i < self->array->len; i++) arraymp[i] = -arraymp[i];
|
||||
}
|
||||
return MP_OBJ_FROM_PTR(ndarray);
|
||||
break;
|
||||
|
|
@ -891,19 +900,19 @@ mp_obj_t ndarray_unary_op(mp_unary_op_t op, mp_obj_t self_in) {
|
|||
}
|
||||
ndarray = MP_OBJ_TO_PTR(ndarray_copy(self_in));
|
||||
if(self->array->typecode == NDARRAY_INT8) {
|
||||
int8_t *array = (int8_t *)ndarray->array->items;
|
||||
array8i = (int8_t *)ndarray->array->items;
|
||||
for(size_t i=0; i < self->array->len; i++) {
|
||||
if(array[i] < 0) array[i] = -array[i];
|
||||
if(array8i[i] < 0) array8i[i] = -array8i[i];
|
||||
}
|
||||
} else if(self->array->typecode == NDARRAY_INT16) {
|
||||
int16_t *array = (int16_t *)ndarray->array->items;
|
||||
array16i = (int16_t *)ndarray->array->items;
|
||||
for(size_t i=0; i < self->array->len; i++) {
|
||||
if(array[i] < 0) array[i] = -array[i];
|
||||
if(array16i[i] < 0) array16i[i] = -array16i[i];
|
||||
}
|
||||
} else {
|
||||
mp_float_t *array = (mp_float_t *)ndarray->array->items;
|
||||
arraymp = (mp_float_t *)ndarray->array->items;
|
||||
for(size_t i=0; i < self->array->len; i++) {
|
||||
if(array[i] < 0) array[i] = -array[i];
|
||||
if(arraymp[i] < 0) arraymp[i] = -arraymp[i];
|
||||
}
|
||||
}
|
||||
return MP_OBJ_FROM_PTR(ndarray);
|
||||
|
|
@ -911,67 +920,3 @@ mp_obj_t ndarray_unary_op(mp_unary_op_t op, mp_obj_t self_in) {
|
|||
default: return MP_OBJ_NULL; // operator not supported
|
||||
}
|
||||
}
|
||||
|
||||
mp_obj_t ndarray_transpose(mp_obj_t self_in) {
|
||||
ndarray_obj_t *self = MP_OBJ_TO_PTR(self_in);
|
||||
// the size of a single item in the array
|
||||
uint8_t _sizeof = mp_binary_get_size('@', self->array->typecode, NULL);
|
||||
|
||||
// NOTE:
|
||||
// if the matrices are square, we can simply swap items, but
|
||||
// generic matrices can't be transposed in place, so we have to
|
||||
// declare a temporary variable
|
||||
|
||||
// NOTE:
|
||||
// In the old matrix, the coordinate (m, n) is m*self->n + n
|
||||
// We have to assign this to the coordinate (n, m) in the new
|
||||
// matrix, i.e., to n*self->m + m (since the new matrix has self->m columns)
|
||||
|
||||
// one-dimensional arrays can be transposed by simply swapping the dimensions
|
||||
if((self->m != 1) && (self->n != 1)) {
|
||||
uint8_t *c = (uint8_t *)self->array->items;
|
||||
// self->bytes is the size of the bytearray, irrespective of the typecode
|
||||
uint8_t *tmp = m_new(uint8_t, self->bytes);
|
||||
for(size_t m=0; m < self->m; m++) {
|
||||
for(size_t n=0; n < self->n; n++) {
|
||||
memcpy(tmp+_sizeof*(n*self->m + m), c+_sizeof*(m*self->n + n), _sizeof);
|
||||
}
|
||||
}
|
||||
memcpy(self->array->items, tmp, self->bytes);
|
||||
m_del(uint8_t, tmp, self->bytes);
|
||||
}
|
||||
SWAP(size_t, self->m, self->n);
|
||||
return mp_const_none;
|
||||
}
|
||||
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(ndarray_transpose_obj, ndarray_transpose);
|
||||
|
||||
mp_obj_t ndarray_reshape(mp_obj_t self_in, mp_obj_t shape) {
|
||||
ndarray_obj_t *self = MP_OBJ_TO_PTR(self_in);
|
||||
if(!MP_OBJ_IS_TYPE(shape, &mp_type_tuple) || (MP_OBJ_SMALL_INT_VALUE(mp_obj_len_maybe(shape)) != 2)) {
|
||||
mp_raise_ValueError(translate("shape must be a 2-tuple"));
|
||||
}
|
||||
|
||||
mp_obj_iter_buf_t iter_buf;
|
||||
mp_obj_t item, iterable = mp_getiter(shape, &iter_buf);
|
||||
uint16_t m, n;
|
||||
item = mp_iternext(iterable);
|
||||
m = mp_obj_get_int(item);
|
||||
item = mp_iternext(iterable);
|
||||
n = mp_obj_get_int(item);
|
||||
if(m*n != self->m*self->n) {
|
||||
// TODO: the proper error message would be "cannot reshape array of size %d into shape (%d, %d)"
|
||||
mp_raise_ValueError(translate("cannot reshape array (incompatible input/output shape)"));
|
||||
}
|
||||
self->m = m;
|
||||
self->n = n;
|
||||
return MP_OBJ_FROM_PTR(self);
|
||||
}
|
||||
|
||||
MP_DEFINE_CONST_FUN_OBJ_2(ndarray_reshape_obj, ndarray_reshape);
|
||||
|
||||
mp_int_t ndarray_get_buffer(mp_obj_t self_in, mp_buffer_info_t *bufinfo, mp_uint_t flags) {
|
||||
ndarray_obj_t *self = MP_OBJ_TO_PTR(self_in);
|
||||
// buffer_p.get_buffer() returns zero for success, while mp_get_buffer returns true for success
|
||||
return !mp_get_buffer(self->array, bufinfo, flags);
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,4 +1,3 @@
|
|||
|
||||
/*
|
||||
* This file is part of the micropython-ulab project,
|
||||
*
|
||||
|
|
@ -6,9 +5,9 @@
|
|||
*
|
||||
* The MIT License (MIT)
|
||||
*
|
||||
* Copyright (c) 2019-2020 Zoltán Vörös
|
||||
* Copyright (c) 2019 Zoltán Vörös
|
||||
*/
|
||||
|
||||
|
||||
#ifndef _NDARRAY_
|
||||
#define _NDARRAY_
|
||||
|
||||
|
|
@ -25,13 +24,7 @@
|
|||
#define FLOAT_TYPECODE 'd'
|
||||
#endif
|
||||
|
||||
#if !CIRCUITPY
|
||||
#define translate(x) x
|
||||
#endif
|
||||
|
||||
#define SWAP(t, a, b) { t tmp = a; a = b; b = tmp; }
|
||||
|
||||
extern const mp_obj_type_t ulab_ndarray_type;
|
||||
const mp_obj_type_t ulab_ndarray_type;
|
||||
|
||||
enum NDARRAY_TYPE {
|
||||
NDARRAY_UINT8 = 'B',
|
||||
|
|
@ -60,30 +53,16 @@ void ndarray_assign_elements(mp_obj_array_t *, mp_obj_t , uint8_t , size_t *);
|
|||
ndarray_obj_t *create_new_ndarray(size_t , size_t , uint8_t );
|
||||
|
||||
mp_obj_t ndarray_copy(mp_obj_t );
|
||||
#ifdef CIRCUITPY
|
||||
mp_obj_t ndarray_make_new(const mp_obj_type_t *type, size_t n_args, const mp_obj_t *args, mp_map_t *kw_args);
|
||||
#else
|
||||
mp_obj_t ndarray_make_new(const mp_obj_type_t *, size_t , size_t , const mp_obj_t *);
|
||||
#endif
|
||||
mp_obj_t ndarray_make_new(const mp_obj_type_t *, size_t, const mp_obj_t *, mp_map_t *);
|
||||
mp_obj_t ndarray_subscr(mp_obj_t , mp_obj_t , mp_obj_t );
|
||||
mp_obj_t ndarray_getiter(mp_obj_t , mp_obj_iter_buf_t *);
|
||||
mp_obj_t ndarray_binary_op(mp_binary_op_t , mp_obj_t , mp_obj_t );
|
||||
mp_obj_t ndarray_unary_op(mp_unary_op_t , mp_obj_t );
|
||||
|
||||
mp_obj_t ndarray_shape(mp_obj_t );
|
||||
mp_obj_t ndarray_size(mp_obj_t );
|
||||
mp_obj_t ndarray_itemsize(mp_obj_t );
|
||||
mp_obj_t ndarray_rawsize(mp_obj_t );
|
||||
mp_obj_t ndarray_flatten(size_t , const mp_obj_t *, mp_map_t *);
|
||||
|
||||
mp_obj_t ndarray_reshape(mp_obj_t , mp_obj_t );
|
||||
MP_DECLARE_CONST_FUN_OBJ_2(ndarray_reshape_obj);
|
||||
|
||||
mp_obj_t ndarray_transpose(mp_obj_t );
|
||||
MP_DECLARE_CONST_FUN_OBJ_1(ndarray_transpose_obj);
|
||||
|
||||
mp_int_t ndarray_get_buffer(mp_obj_t obj, mp_buffer_info_t *bufinfo, mp_uint_t flags);
|
||||
//void ndarray_attributes(mp_obj_t , qstr , mp_obj_t *);
|
||||
|
||||
mp_obj_t ndarray_asbytearray(mp_obj_t );
|
||||
|
||||
#define CREATE_SINGLE_ITEM(outarray, type, typecode, value) do {\
|
||||
ndarray_obj_t *tmp = create_new_ndarray(1, 1, (typecode));\
|
||||
|
|
|
|||
|
|
@ -1,62 +0,0 @@
|
|||
|
||||
/*
|
||||
* This file is part of the micropython-ulab project,
|
||||
*
|
||||
* https://github.com/v923z/micropython-ulab
|
||||
*
|
||||
* The MIT License (MIT)
|
||||
*
|
||||
* Copyright (c) 2020 Jeff Epler for Adafruit Industries
|
||||
* 2020 Zoltán Vörös
|
||||
*/
|
||||
|
||||
#ifndef _NDARRAY_PROPERTIES_
|
||||
#define _NDARRAY_PROPERTIES_
|
||||
|
||||
#include "py/runtime.h"
|
||||
#include "py/binary.h"
|
||||
#include "py/obj.h"
|
||||
#include "py/objarray.h"
|
||||
|
||||
#include "ndarray.h"
|
||||
|
||||
typedef struct _mp_obj_property_t {
|
||||
mp_obj_base_t base;
|
||||
mp_obj_t proxy[3]; // getter, setter, deleter
|
||||
} mp_obj_property_t;
|
||||
|
||||
/* v923z: it is not at all clear to me, why this must be declared; it should already be in obj.h */
|
||||
typedef struct _mp_obj_none_t {
|
||||
mp_obj_base_t base;
|
||||
} mp_obj_none_t;
|
||||
|
||||
const mp_obj_type_t mp_type_NoneType;
|
||||
const mp_obj_none_t mp_const_none_obj = {{&mp_type_NoneType}};
|
||||
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(ndarray_get_shape_obj, ndarray_shape);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(ndarray_get_size_obj, ndarray_size);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(ndarray_get_itemsize_obj, ndarray_itemsize);
|
||||
MP_DEFINE_CONST_FUN_OBJ_KW(ndarray_flatten_obj, 1, ndarray_flatten);
|
||||
|
||||
STATIC const mp_obj_property_t ndarray_shape_obj = {
|
||||
.base.type = &mp_type_property,
|
||||
.proxy = {(mp_obj_t)&ndarray_get_shape_obj,
|
||||
(mp_obj_t)&mp_const_none_obj,
|
||||
(mp_obj_t)&mp_const_none_obj},
|
||||
};
|
||||
|
||||
STATIC const mp_obj_property_t ndarray_size_obj = {
|
||||
.base.type = &mp_type_property,
|
||||
.proxy = {(mp_obj_t)&ndarray_get_size_obj,
|
||||
(mp_obj_t)&mp_const_none_obj,
|
||||
(mp_obj_t)&mp_const_none_obj},
|
||||
};
|
||||
|
||||
STATIC const mp_obj_property_t ndarray_itemsize_obj = {
|
||||
.base.type = &mp_type_property,
|
||||
.proxy = {(mp_obj_t)&ndarray_get_itemsize_obj,
|
||||
(mp_obj_t)&mp_const_none_obj,
|
||||
(mp_obj_t)&mp_const_none_obj},
|
||||
};
|
||||
|
||||
#endif
|
||||
108
code/numerical.c
108
code/numerical.c
|
|
@ -1,4 +1,3 @@
|
|||
|
||||
/*
|
||||
* This file is part of the micropython-ulab project,
|
||||
*
|
||||
|
|
@ -8,7 +7,7 @@
|
|||
*
|
||||
* Copyright (c) 2019-2020 Zoltán Vörös
|
||||
*/
|
||||
|
||||
|
||||
#include <math.h>
|
||||
#include <stdlib.h>
|
||||
#include <string.h>
|
||||
|
|
@ -17,10 +16,9 @@
|
|||
#include "py/runtime.h"
|
||||
#include "py/builtin.h"
|
||||
#include "py/misc.h"
|
||||
#include "compat.h"
|
||||
#include "numerical.h"
|
||||
|
||||
#if ULAB_NUMERICAL_MODULE
|
||||
|
||||
enum NUMERICAL_FUNCTION_TYPE {
|
||||
NUMERICAL_MIN,
|
||||
NUMERICAL_MAX,
|
||||
|
|
@ -33,11 +31,11 @@ enum NUMERICAL_FUNCTION_TYPE {
|
|||
|
||||
mp_obj_t numerical_linspace(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
|
||||
static const mp_arg_t allowed_args[] = {
|
||||
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = mp_const_none } },
|
||||
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = mp_const_none } },
|
||||
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_NONE } },
|
||||
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_NONE } },
|
||||
{ MP_QSTR_num, MP_ARG_INT, {.u_int = 50} },
|
||||
{ MP_QSTR_endpoint, MP_ARG_KW_ONLY | MP_ARG_OBJ, {.u_rom_obj = mp_const_true} },
|
||||
{ MP_QSTR_retstep, MP_ARG_KW_ONLY | MP_ARG_OBJ, {.u_rom_obj = mp_const_false} },
|
||||
{ MP_QSTR_endpoint, MP_ARG_KW_ONLY | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_TRUE} },
|
||||
{ MP_QSTR_retstep, MP_ARG_KW_ONLY | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_FALSE} },
|
||||
{ MP_QSTR_dtype, MP_ARG_KW_ONLY | MP_ARG_INT, {.u_int = NDARRAY_FLOAT} },
|
||||
};
|
||||
|
||||
|
|
@ -80,8 +78,6 @@ mp_obj_t numerical_linspace(size_t n_args, const mp_obj_t *pos_args, mp_map_t *k
|
|||
}
|
||||
}
|
||||
|
||||
MP_DEFINE_CONST_FUN_OBJ_KW(numerical_linspace_obj, 2, numerical_linspace);
|
||||
|
||||
void axis_sorter(ndarray_obj_t *ndarray, mp_obj_t axis, size_t *m, size_t *n, size_t *N,
|
||||
size_t *increment, size_t *len, size_t *start_inc) {
|
||||
if(axis == mp_const_none) { // flatten the array
|
||||
|
|
@ -257,8 +253,8 @@ mp_obj_t numerical_argmin_argmax_ndarray(ndarray_obj_t *ndarray, mp_obj_t axis,
|
|||
|
||||
STATIC mp_obj_t numerical_function(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args, uint8_t optype) {
|
||||
static const mp_arg_t allowed_args[] = {
|
||||
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = mp_const_none} } ,
|
||||
{ MP_QSTR_axis, MP_ARG_OBJ, {.u_rom_obj = mp_const_none } },
|
||||
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_NONE} } ,
|
||||
{ MP_QSTR_axis, MP_ARG_OBJ, {.u_rom_obj = MP_ROM_NONE } },
|
||||
};
|
||||
|
||||
mp_arg_val_t args[MP_ARRAY_SIZE(allowed_args)];
|
||||
|
|
@ -309,42 +305,30 @@ mp_obj_t numerical_min(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_arg
|
|||
return numerical_function(n_args, pos_args, kw_args, NUMERICAL_MIN);
|
||||
}
|
||||
|
||||
MP_DEFINE_CONST_FUN_OBJ_KW(numerical_min_obj, 1, numerical_min);
|
||||
|
||||
mp_obj_t numerical_max(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
|
||||
return numerical_function(n_args, pos_args, kw_args, NUMERICAL_MAX);
|
||||
}
|
||||
|
||||
MP_DEFINE_CONST_FUN_OBJ_KW(numerical_max_obj, 1, numerical_max);
|
||||
|
||||
mp_obj_t numerical_argmin(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
|
||||
return numerical_function(n_args, pos_args, kw_args, NUMERICAL_ARGMIN);
|
||||
}
|
||||
|
||||
MP_DEFINE_CONST_FUN_OBJ_KW(numerical_argmin_obj, 1, numerical_argmin);
|
||||
|
||||
mp_obj_t numerical_argmax(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
|
||||
return numerical_function(n_args, pos_args, kw_args, NUMERICAL_ARGMAX);
|
||||
}
|
||||
|
||||
MP_DEFINE_CONST_FUN_OBJ_KW(numerical_argmax_obj, 1, numerical_argmax);
|
||||
|
||||
mp_obj_t numerical_sum(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
|
||||
return numerical_function(n_args, pos_args, kw_args, NUMERICAL_SUM);
|
||||
}
|
||||
|
||||
MP_DEFINE_CONST_FUN_OBJ_KW(numerical_sum_obj, 1, numerical_sum);
|
||||
|
||||
mp_obj_t numerical_mean(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
|
||||
return numerical_function(n_args, pos_args, kw_args, NUMERICAL_MEAN);
|
||||
}
|
||||
|
||||
MP_DEFINE_CONST_FUN_OBJ_KW(numerical_mean_obj, 1, numerical_mean);
|
||||
|
||||
mp_obj_t numerical_std(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
|
||||
static const mp_arg_t allowed_args[] = {
|
||||
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = mp_const_none } } ,
|
||||
{ MP_QSTR_axis, MP_ARG_OBJ, {.u_rom_obj = mp_const_none } },
|
||||
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_NONE } } ,
|
||||
{ MP_QSTR_axis, MP_ARG_OBJ, {.u_rom_obj = MP_ROM_NONE } },
|
||||
{ MP_QSTR_ddof, MP_ARG_KW_ONLY | MP_ARG_INT, {.u_int = 0} },
|
||||
};
|
||||
|
||||
|
|
@ -369,13 +353,11 @@ mp_obj_t numerical_std(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_arg
|
|||
return mp_const_none;
|
||||
}
|
||||
|
||||
MP_DEFINE_CONST_FUN_OBJ_KW(numerical_std_obj, 1, numerical_std);
|
||||
|
||||
mp_obj_t numerical_roll(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
|
||||
static const mp_arg_t allowed_args[] = {
|
||||
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = mp_const_none } },
|
||||
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = mp_const_none } },
|
||||
{ MP_QSTR_axis, MP_ARG_KW_ONLY | MP_ARG_OBJ, {.u_rom_obj = mp_const_none } },
|
||||
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_NONE } },
|
||||
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_NONE } },
|
||||
{ MP_QSTR_axis, MP_ARG_KW_ONLY | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_NONE } },
|
||||
};
|
||||
|
||||
mp_arg_val_t args[MP_ARRAY_SIZE(allowed_args)];
|
||||
|
|
@ -451,18 +433,16 @@ mp_obj_t numerical_roll(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_ar
|
|||
}
|
||||
}
|
||||
|
||||
MP_DEFINE_CONST_FUN_OBJ_KW(numerical_roll_obj, 2, numerical_roll);
|
||||
|
||||
mp_obj_t numerical_flip(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
|
||||
static const mp_arg_t allowed_args[] = {
|
||||
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = mp_const_none } },
|
||||
{ MP_QSTR_axis, MP_ARG_KW_ONLY | MP_ARG_OBJ, {.u_rom_obj = mp_const_none } },
|
||||
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_NONE } },
|
||||
{ MP_QSTR_axis, MP_ARG_KW_ONLY | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_NONE } },
|
||||
};
|
||||
|
||||
mp_arg_val_t args[MP_ARRAY_SIZE(allowed_args)];
|
||||
mp_arg_parse_all(1, pos_args, kw_args, MP_ARRAY_SIZE(allowed_args), allowed_args, args);
|
||||
|
||||
if(!MP_OBJ_IS_TYPE(args[0].u_obj, &ulab_ndarray_type)) {
|
||||
if(!mp_obj_is_type(args[0].u_obj, &ulab_ndarray_type)) {
|
||||
mp_raise_TypeError(translate("flip argument must be an ndarray"));
|
||||
}
|
||||
if((args[1].u_obj != mp_const_none) &&
|
||||
|
|
@ -500,11 +480,9 @@ mp_obj_t numerical_flip(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_ar
|
|||
return out;
|
||||
}
|
||||
|
||||
MP_DEFINE_CONST_FUN_OBJ_KW(numerical_flip_obj, 1, numerical_flip);
|
||||
|
||||
mp_obj_t numerical_diff(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
|
||||
static const mp_arg_t allowed_args[] = {
|
||||
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = mp_const_none } },
|
||||
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_NONE } },
|
||||
{ MP_QSTR_n, MP_ARG_KW_ONLY | MP_ARG_INT, {.u_int = 1 } },
|
||||
{ MP_QSTR_axis, MP_ARG_KW_ONLY | MP_ARG_INT, {.u_int = -1 } },
|
||||
};
|
||||
|
|
@ -512,7 +490,7 @@ mp_obj_t numerical_diff(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_ar
|
|||
mp_arg_val_t args[MP_ARRAY_SIZE(allowed_args)];
|
||||
mp_arg_parse_all(1, pos_args, kw_args, MP_ARRAY_SIZE(allowed_args), allowed_args, args);
|
||||
|
||||
if(!MP_OBJ_IS_TYPE(args[0].u_obj, &ulab_ndarray_type)) {
|
||||
if(!mp_obj_is_type(args[0].u_obj, &ulab_ndarray_type)) {
|
||||
mp_raise_TypeError(translate("diff argument must be an ndarray"));
|
||||
}
|
||||
|
||||
|
|
@ -523,7 +501,7 @@ mp_obj_t numerical_diff(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_ar
|
|||
} else if(args[2].u_int == 0) { // differtiate along vertical axis
|
||||
increment = in->n;
|
||||
} else {
|
||||
mp_raise_ValueError(translate("axis must be -1, 0, or 1"));
|
||||
mp_raise_ValueError(translate("axis must be -1, 0, or 1"));
|
||||
}
|
||||
if((args[1].u_int < 0) || (args[1].u_int > 9)) {
|
||||
mp_raise_ValueError(translate("n must be between 0, and 9"));
|
||||
|
|
@ -570,10 +548,8 @@ mp_obj_t numerical_diff(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_ar
|
|||
return MP_OBJ_FROM_PTR(out);
|
||||
}
|
||||
|
||||
MP_DEFINE_CONST_FUN_OBJ_KW(numerical_diff_obj, 1, numerical_diff);
|
||||
|
||||
mp_obj_t numerical_sort_helper(mp_obj_t oin, mp_obj_t axis, uint8_t inplace) {
|
||||
if(!MP_OBJ_IS_TYPE(oin, &ulab_ndarray_type)) {
|
||||
if(!mp_obj_is_type(oin, &ulab_ndarray_type)) {
|
||||
mp_raise_TypeError(translate("sort argument must be an ndarray"));
|
||||
}
|
||||
|
||||
|
|
@ -605,7 +581,7 @@ mp_obj_t numerical_sort_helper(mp_obj_t oin, mp_obj_t axis, uint8_t inplace) {
|
|||
end = ndarray->m;
|
||||
N = ndarray->m;
|
||||
} else {
|
||||
mp_raise_ValueError(translate("axis must be -1, 0, None, or 1"));
|
||||
mp_raise_ValueError(translate("axis must be -1, 0, None, or 1"));
|
||||
}
|
||||
|
||||
size_t q, k, p, c;
|
||||
|
|
@ -631,7 +607,7 @@ mp_obj_t numerical_sort_helper(mp_obj_t oin, mp_obj_t axis, uint8_t inplace) {
|
|||
// numpy function
|
||||
mp_obj_t numerical_sort(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
|
||||
static const mp_arg_t allowed_args[] = {
|
||||
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = mp_const_none } },
|
||||
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_NONE } },
|
||||
{ MP_QSTR_axis, MP_ARG_KW_ONLY | MP_ARG_OBJ, {.u_int = -1 } },
|
||||
};
|
||||
|
||||
|
|
@ -640,13 +616,10 @@ mp_obj_t numerical_sort(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_ar
|
|||
|
||||
return numerical_sort_helper(args[0].u_obj, args[1].u_obj, 0);
|
||||
}
|
||||
|
||||
MP_DEFINE_CONST_FUN_OBJ_KW(numerical_sort_obj, 1, numerical_sort);
|
||||
|
||||
// method of an ndarray
|
||||
mp_obj_t numerical_sort_inplace(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
|
||||
static const mp_arg_t allowed_args[] = {
|
||||
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = mp_const_none } },
|
||||
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_NONE } },
|
||||
{ MP_QSTR_axis, MP_ARG_KW_ONLY | MP_ARG_OBJ, {.u_int = -1 } },
|
||||
};
|
||||
|
||||
|
|
@ -656,16 +629,14 @@ mp_obj_t numerical_sort_inplace(size_t n_args, const mp_obj_t *pos_args, mp_map_
|
|||
return numerical_sort_helper(args[0].u_obj, args[1].u_obj, 1);
|
||||
}
|
||||
|
||||
MP_DEFINE_CONST_FUN_OBJ_KW(numerical_sort_inplace_obj, 1, numerical_sort_inplace);
|
||||
|
||||
mp_obj_t numerical_argsort(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
|
||||
static const mp_arg_t allowed_args[] = {
|
||||
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = mp_const_none } },
|
||||
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_NONE } },
|
||||
{ MP_QSTR_axis, MP_ARG_KW_ONLY | MP_ARG_OBJ, {.u_int = -1 } },
|
||||
};
|
||||
mp_arg_val_t args[MP_ARRAY_SIZE(allowed_args)];
|
||||
mp_arg_parse_all(1, pos_args, kw_args, MP_ARRAY_SIZE(allowed_args), allowed_args, args);
|
||||
if(!MP_OBJ_IS_TYPE(args[0].u_obj, &ulab_ndarray_type)) {
|
||||
if(!mp_obj_is_type(args[0].u_obj, &ulab_ndarray_type)) {
|
||||
mp_raise_TypeError(translate("argsort argument must be an ndarray"));
|
||||
}
|
||||
|
||||
|
|
@ -727,32 +698,3 @@ mp_obj_t numerical_argsort(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw
|
|||
}
|
||||
return MP_OBJ_FROM_PTR(indices);
|
||||
}
|
||||
|
||||
MP_DEFINE_CONST_FUN_OBJ_KW(numerical_argsort_obj, 1, numerical_argsort);
|
||||
|
||||
#if !CIRCUITPY
|
||||
STATIC const mp_rom_map_elem_t ulab_numerical_globals_table[] = {
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_linspace), (mp_obj_t)&numerical_linspace_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_sum), (mp_obj_t)&numerical_sum_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_mean), (mp_obj_t)&numerical_mean_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_std), (mp_obj_t)&numerical_std_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_min), (mp_obj_t)&numerical_min_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_max), (mp_obj_t)&numerical_max_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_argmin), (mp_obj_t)&numerical_argmin_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_argmax), (mp_obj_t)&numerical_argmax_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_roll), (mp_obj_t)&numerical_roll_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_flip), (mp_obj_t)&numerical_flip_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_diff), (mp_obj_t)&numerical_diff_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_sort), (mp_obj_t)&numerical_sort_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_argsort), (mp_obj_t)&numerical_argsort_obj },
|
||||
};
|
||||
|
||||
STATIC MP_DEFINE_CONST_DICT(mp_module_ulab_numerical_globals, ulab_numerical_globals_table);
|
||||
|
||||
mp_obj_module_t ulab_numerical_module = {
|
||||
.base = { &mp_type_module },
|
||||
.globals = (mp_obj_dict_t*)&mp_module_ulab_numerical_globals,
|
||||
};
|
||||
#endif
|
||||
|
||||
#endif
|
||||
|
|
|
|||
|
|
@ -1,4 +1,3 @@
|
|||
|
||||
/*
|
||||
* This file is part of the micropython-ulab project,
|
||||
*
|
||||
|
|
@ -8,21 +7,31 @@
|
|||
*
|
||||
* Copyright (c) 2019-2020 Zoltán Vörös
|
||||
*/
|
||||
|
||||
|
||||
#ifndef _NUMERICAL_
|
||||
#define _NUMERICAL_
|
||||
|
||||
#include "ulab.h"
|
||||
#include "ndarray.h"
|
||||
|
||||
#if ULAB_NUMERICAL_MODULE
|
||||
|
||||
extern mp_obj_module_t ulab_numerical_module;
|
||||
mp_obj_t numerical_linspace(size_t , const mp_obj_t *, mp_map_t *);
|
||||
mp_obj_t numerical_sum(size_t , const mp_obj_t *, mp_map_t *);
|
||||
mp_obj_t numerical_mean(size_t , const mp_obj_t *, mp_map_t *);
|
||||
mp_obj_t numerical_std(size_t , const mp_obj_t *, mp_map_t *);
|
||||
mp_obj_t numerical_min(size_t , const mp_obj_t *, mp_map_t *);
|
||||
mp_obj_t numerical_max(size_t , const mp_obj_t *, mp_map_t *);
|
||||
mp_obj_t numerical_argmin(size_t , const mp_obj_t *, mp_map_t *);
|
||||
mp_obj_t numerical_argmax(size_t , const mp_obj_t *, mp_map_t *);
|
||||
mp_obj_t numerical_roll(size_t , const mp_obj_t *, mp_map_t *);
|
||||
|
||||
// TODO: implement minimum/maximum, and cumsum
|
||||
//mp_obj_t numerical_minimum(mp_obj_t , mp_obj_t );
|
||||
//mp_obj_t numerical_maximum(mp_obj_t , mp_obj_t );
|
||||
//mp_obj_t numerical_cumsum(size_t , const mp_obj_t *, mp_map_t *);
|
||||
mp_obj_t numerical_minimum(mp_obj_t , mp_obj_t );
|
||||
mp_obj_t numerical_maximum(mp_obj_t , mp_obj_t );
|
||||
mp_obj_t numerical_cumsum(size_t , const mp_obj_t *, mp_map_t *);
|
||||
mp_obj_t numerical_flip(size_t , const mp_obj_t *, mp_map_t *);
|
||||
mp_obj_t numerical_diff(size_t , const mp_obj_t *, mp_map_t *);
|
||||
mp_obj_t numerical_sort(size_t , const mp_obj_t *, mp_map_t *);
|
||||
mp_obj_t numerical_sort_inplace(size_t , const mp_obj_t *, mp_map_t *);
|
||||
mp_obj_t numerical_argsort(size_t , const mp_obj_t *, mp_map_t *);
|
||||
|
||||
#define RUN_ARGMIN(in, out, typein, typeout, len, start, increment, op, pos) do {\
|
||||
typein *array = (typein *)(in)->array->items;\
|
||||
|
|
@ -148,20 +157,4 @@ extern mp_obj_module_t ulab_numerical_module;
|
|||
}\
|
||||
} while(0)
|
||||
|
||||
MP_DECLARE_CONST_FUN_OBJ_KW(numerical_linspace_obj);
|
||||
MP_DECLARE_CONST_FUN_OBJ_KW(numerical_min_obj);
|
||||
MP_DECLARE_CONST_FUN_OBJ_KW(numerical_max_obj);
|
||||
MP_DECLARE_CONST_FUN_OBJ_KW(numerical_argmin_obj);
|
||||
MP_DECLARE_CONST_FUN_OBJ_KW(numerical_argmax_obj);
|
||||
MP_DECLARE_CONST_FUN_OBJ_KW(numerical_sum_obj);
|
||||
MP_DECLARE_CONST_FUN_OBJ_KW(numerical_mean_obj);
|
||||
MP_DECLARE_CONST_FUN_OBJ_KW(numerical_std_obj);
|
||||
MP_DECLARE_CONST_FUN_OBJ_KW(numerical_roll_obj);
|
||||
MP_DECLARE_CONST_FUN_OBJ_KW(numerical_flip_obj);
|
||||
MP_DECLARE_CONST_FUN_OBJ_KW(numerical_diff_obj);
|
||||
MP_DECLARE_CONST_FUN_OBJ_KW(numerical_sort_obj);
|
||||
MP_DECLARE_CONST_FUN_OBJ_KW(numerical_sort_inplace_obj);
|
||||
MP_DECLARE_CONST_FUN_OBJ_KW(numerical_argsort_obj);
|
||||
|
||||
#endif
|
||||
#endif
|
||||
|
|
|
|||
33
code/poly.c
33
code/poly.c
|
|
@ -1,4 +1,3 @@
|
|||
|
||||
/*
|
||||
* This file is part of the micropython-ulab project,
|
||||
*
|
||||
|
|
@ -8,15 +7,16 @@
|
|||
*
|
||||
* Copyright (c) 2019-2020 Zoltán Vörös
|
||||
*/
|
||||
|
||||
|
||||
#include "py/obj.h"
|
||||
#include "py/runtime.h"
|
||||
#include "py/objarray.h"
|
||||
#include "compat.h"
|
||||
#include "ndarray.h"
|
||||
#include "linalg.h"
|
||||
#include "poly.h"
|
||||
|
||||
#if ULAB_POLY_MODULE
|
||||
|
||||
bool object_is_nditerable(mp_obj_t o_in) {
|
||||
if(MP_OBJ_IS_TYPE(o_in, &ulab_ndarray_type) ||
|
||||
MP_OBJ_IS_TYPE(o_in, &mp_type_tuple) ||
|
||||
|
|
@ -41,7 +41,7 @@ mp_obj_t poly_polyval(mp_obj_t o_p, mp_obj_t o_x) {
|
|||
// TODO: there is a bug here: matrices won't work,
|
||||
// because there is a single iteration loop
|
||||
size_t m, n;
|
||||
if(MP_OBJ_IS_TYPE(o_x, &ulab_ndarray_type)) {
|
||||
if(mp_obj_is_type(o_x, &ulab_ndarray_type)) {
|
||||
ndarray_obj_t *ndx = MP_OBJ_TO_PTR(o_x);
|
||||
m = ndx->m;
|
||||
n = ndx->n;
|
||||
|
|
@ -83,8 +83,6 @@ mp_obj_t poly_polyval(mp_obj_t o_p, mp_obj_t o_x) {
|
|||
return MP_OBJ_FROM_PTR(out);
|
||||
}
|
||||
|
||||
MP_DEFINE_CONST_FUN_OBJ_2(poly_polyval_obj, poly_polyval);
|
||||
|
||||
mp_obj_t poly_polyfit(size_t n_args, const mp_obj_t *args) {
|
||||
if((n_args != 2) && (n_args != 3)) {
|
||||
mp_raise_ValueError(translate("number of arguments must be 2, or 3"));
|
||||
|
|
@ -92,8 +90,8 @@ mp_obj_t poly_polyfit(size_t n_args, const mp_obj_t *args) {
|
|||
if(!object_is_nditerable(args[0])) {
|
||||
mp_raise_ValueError(translate("input data must be an iterable"));
|
||||
}
|
||||
uint16_t lenx = 0, leny = 0;
|
||||
uint8_t deg = 0;
|
||||
uint16_t lenx, leny;
|
||||
uint8_t deg;
|
||||
mp_float_t *x, *XT, *y, *prod;
|
||||
|
||||
if(n_args == 2) { // only the y values are supplied
|
||||
|
|
@ -194,22 +192,3 @@ mp_obj_t poly_polyfit(size_t n_args, const mp_obj_t *args) {
|
|||
}
|
||||
return MP_OBJ_FROM_PTR(beta);
|
||||
}
|
||||
|
||||
MP_DEFINE_CONST_FUN_OBJ_VAR_BETWEEN(poly_polyfit_obj, 2, 3, poly_polyfit);
|
||||
|
||||
#if !CIRCUITPY
|
||||
STATIC const mp_rom_map_elem_t ulab_poly_globals_table[] = {
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR___name__), MP_OBJ_NEW_QSTR(MP_QSTR_poly) },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_polyval), (mp_obj_t)&poly_polyval_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_polyfit), (mp_obj_t)&poly_polyfit_obj },
|
||||
};
|
||||
|
||||
STATIC MP_DEFINE_CONST_DICT(mp_module_ulab_poly_globals, ulab_poly_globals_table);
|
||||
|
||||
mp_obj_module_t ulab_poly_module = {
|
||||
.base = { &mp_type_module },
|
||||
.globals = (mp_obj_dict_t*)&mp_module_ulab_poly_globals,
|
||||
};
|
||||
#endif
|
||||
|
||||
#endif
|
||||
|
|
|
|||
16
code/poly.h
16
code/poly.h
|
|
@ -1,4 +1,3 @@
|
|||
|
||||
/*
|
||||
* This file is part of the micropython-ulab project,
|
||||
*
|
||||
|
|
@ -6,20 +5,13 @@
|
|||
*
|
||||
* The MIT License (MIT)
|
||||
*
|
||||
* Copyright (c) 2019-2020 Zoltán Vörös
|
||||
* Copyright (c) 2019 Zoltán Vörös
|
||||
*/
|
||||
|
||||
|
||||
#ifndef _POLY_
|
||||
#define _POLY_
|
||||
|
||||
#include "ulab.h"
|
||||
|
||||
#if ULAB_POLY_MODULE
|
||||
|
||||
extern mp_obj_module_t ulab_poly_module;
|
||||
|
||||
MP_DECLARE_CONST_FUN_OBJ_2(poly_polyval_obj);
|
||||
MP_DECLARE_CONST_FUN_OBJ_VAR_BETWEEN(poly_polyfit_obj);
|
||||
mp_obj_t poly_polyval(mp_obj_t , mp_obj_t );
|
||||
mp_obj_t poly_polyfit(size_t , const mp_obj_t *);
|
||||
|
||||
#endif
|
||||
#endif
|
||||
|
|
|
|||
161
code/ulab.c
161
code/ulab.c
|
|
@ -1,6 +1,5 @@
|
|||
|
||||
/*
|
||||
* This file is part of the micropython-ulab project,
|
||||
* This file is part of the micropython-ulab project,
|
||||
*
|
||||
* https://github.com/v923z/micropython-ulab
|
||||
*
|
||||
|
|
@ -8,7 +7,7 @@
|
|||
*
|
||||
* Copyright (c) 2019-2020 Zoltán Vörös
|
||||
*/
|
||||
|
||||
|
||||
#include <math.h>
|
||||
#include <stdio.h>
|
||||
#include <stdlib.h>
|
||||
|
|
@ -18,27 +17,86 @@
|
|||
#include "py/obj.h"
|
||||
#include "py/objarray.h"
|
||||
|
||||
#include "ulab.h"
|
||||
#include "compat.h"
|
||||
#include "ndarray.h"
|
||||
#include "ndarray_properties.h"
|
||||
#include "linalg.h"
|
||||
#include "vectorise.h"
|
||||
#include "poly.h"
|
||||
#include "fft.h"
|
||||
#include "filter.h"
|
||||
#include "numerical.h"
|
||||
#include "extras.h"
|
||||
|
||||
STATIC MP_DEFINE_STR_OBJ(ulab_version_obj, "0.34.0");
|
||||
STATIC MP_DEFINE_STR_OBJ(ulab_version_obj, "0.26.7");
|
||||
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(ndarray_shape_obj, ndarray_shape);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(ndarray_rawsize_obj, ndarray_rawsize);
|
||||
MP_DEFINE_CONST_FUN_OBJ_KW(ndarray_flatten_obj, 1, ndarray_flatten);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(ndarray_asbytearray_obj, ndarray_asbytearray);
|
||||
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(linalg_transpose_obj, linalg_transpose);
|
||||
MP_DEFINE_CONST_FUN_OBJ_2(linalg_reshape_obj, linalg_reshape);
|
||||
MP_DEFINE_CONST_FUN_OBJ_KW(linalg_size_obj, 1, linalg_size);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(linalg_inv_obj, linalg_inv);
|
||||
MP_DEFINE_CONST_FUN_OBJ_2(linalg_dot_obj, linalg_dot);
|
||||
MP_DEFINE_CONST_FUN_OBJ_KW(linalg_zeros_obj, 0, linalg_zeros);
|
||||
MP_DEFINE_CONST_FUN_OBJ_KW(linalg_ones_obj, 0, linalg_ones);
|
||||
MP_DEFINE_CONST_FUN_OBJ_KW(linalg_eye_obj, 0, linalg_eye);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(linalg_det_obj, linalg_det);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(linalg_eig_obj, linalg_eig);
|
||||
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_acos_obj, vectorise_acos);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_acosh_obj, vectorise_acosh);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_asin_obj, vectorise_asin);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_asinh_obj, vectorise_asinh);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_atan_obj, vectorise_atan);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_atanh_obj, vectorise_atanh);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_ceil_obj, vectorise_ceil);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_cos_obj, vectorise_cos);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_erf_obj, vectorise_erf);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_erfc_obj, vectorise_erfc);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_exp_obj, vectorise_exp);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_expm1_obj, vectorise_expm1);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_floor_obj, vectorise_floor);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_gamma_obj, vectorise_gamma);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_lgamma_obj, vectorise_lgamma);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_log_obj, vectorise_log);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_log10_obj, vectorise_log10);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_log2_obj, vectorise_log2);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_sin_obj, vectorise_sin);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_sinh_obj, vectorise_sinh);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_sqrt_obj, vectorise_sqrt);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_tan_obj, vectorise_tan);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_tanh_obj, vectorise_tanh);
|
||||
|
||||
STATIC MP_DEFINE_CONST_FUN_OBJ_KW(numerical_linspace_obj, 2, numerical_linspace);
|
||||
STATIC MP_DEFINE_CONST_FUN_OBJ_KW(numerical_sum_obj, 1, numerical_sum);
|
||||
STATIC MP_DEFINE_CONST_FUN_OBJ_KW(numerical_mean_obj, 1, numerical_mean);
|
||||
STATIC MP_DEFINE_CONST_FUN_OBJ_KW(numerical_std_obj, 1, numerical_std);
|
||||
STATIC MP_DEFINE_CONST_FUN_OBJ_KW(numerical_min_obj, 1, numerical_min);
|
||||
STATIC MP_DEFINE_CONST_FUN_OBJ_KW(numerical_max_obj, 1, numerical_max);
|
||||
STATIC MP_DEFINE_CONST_FUN_OBJ_KW(numerical_argmin_obj, 1, numerical_argmin);
|
||||
STATIC MP_DEFINE_CONST_FUN_OBJ_KW(numerical_argmax_obj, 1, numerical_argmax);
|
||||
STATIC MP_DEFINE_CONST_FUN_OBJ_KW(numerical_roll_obj, 2, numerical_roll);
|
||||
STATIC MP_DEFINE_CONST_FUN_OBJ_KW(numerical_flip_obj, 1, numerical_flip);
|
||||
STATIC MP_DEFINE_CONST_FUN_OBJ_KW(numerical_diff_obj, 1, numerical_diff);
|
||||
STATIC MP_DEFINE_CONST_FUN_OBJ_KW(numerical_sort_obj, 1, numerical_sort);
|
||||
STATIC MP_DEFINE_CONST_FUN_OBJ_KW(numerical_sort_inplace_obj, 1, numerical_sort_inplace);
|
||||
STATIC MP_DEFINE_CONST_FUN_OBJ_KW(numerical_argsort_obj, 1, numerical_argsort);
|
||||
|
||||
STATIC MP_DEFINE_CONST_FUN_OBJ_2(poly_polyval_obj, poly_polyval);
|
||||
STATIC MP_DEFINE_CONST_FUN_OBJ_VAR_BETWEEN(poly_polyfit_obj, 2, 3, poly_polyfit);
|
||||
|
||||
STATIC MP_DEFINE_CONST_FUN_OBJ_VAR_BETWEEN(fft_fft_obj, 1, 2, fft_fft);
|
||||
STATIC MP_DEFINE_CONST_FUN_OBJ_VAR_BETWEEN(fft_ifft_obj, 1, 2, fft_ifft);
|
||||
STATIC MP_DEFINE_CONST_FUN_OBJ_VAR_BETWEEN(fft_spectrum_obj, 1, 2, fft_spectrum);
|
||||
|
||||
STATIC const mp_rom_map_elem_t ulab_ndarray_locals_dict_table[] = {
|
||||
{ MP_ROM_QSTR(MP_QSTR_flatten), MP_ROM_PTR(&ndarray_flatten_obj) },
|
||||
{ MP_ROM_QSTR(MP_QSTR_reshape), MP_ROM_PTR(&ndarray_reshape_obj) },
|
||||
{ MP_ROM_QSTR(MP_QSTR_transpose), MP_ROM_PTR(&ndarray_transpose_obj) },
|
||||
{ MP_ROM_QSTR(MP_QSTR_shape), MP_ROM_PTR(&ndarray_shape_obj) },
|
||||
{ MP_ROM_QSTR(MP_QSTR_size), MP_ROM_PTR(&ndarray_size_obj) },
|
||||
{ MP_ROM_QSTR(MP_QSTR_itemsize), MP_ROM_PTR(&ndarray_itemsize_obj) },
|
||||
// { MP_ROM_QSTR(MP_QSTR_sort), MP_ROM_PTR(&numerical_sort_inplace_obj) },
|
||||
{ MP_ROM_QSTR(MP_QSTR_rawsize), MP_ROM_PTR(&ndarray_rawsize_obj) },
|
||||
{ MP_ROM_QSTR(MP_QSTR_flatten), MP_ROM_PTR(&ndarray_flatten_obj) },
|
||||
{ MP_ROM_QSTR(MP_QSTR_asbytearray), MP_ROM_PTR(&ndarray_asbytearray_obj) },
|
||||
{ MP_ROM_QSTR(MP_QSTR_transpose), MP_ROM_PTR(&linalg_transpose_obj) },
|
||||
{ MP_ROM_QSTR(MP_QSTR_reshape), MP_ROM_PTR(&linalg_reshape_obj) },
|
||||
{ MP_ROM_QSTR(MP_QSTR_sort), MP_ROM_PTR(&numerical_sort_inplace_obj) },
|
||||
};
|
||||
|
||||
STATIC MP_DEFINE_CONST_DICT(ulab_ndarray_locals_dict, ulab_ndarray_locals_dict_table);
|
||||
|
|
@ -52,36 +110,62 @@ const mp_obj_type_t ulab_ndarray_type = {
|
|||
.getiter = ndarray_getiter,
|
||||
.unary_op = ndarray_unary_op,
|
||||
.binary_op = ndarray_binary_op,
|
||||
.buffer_p = { .get_buffer = ndarray_get_buffer, },
|
||||
.locals_dict = (mp_obj_dict_t*)&ulab_ndarray_locals_dict,
|
||||
};
|
||||
|
||||
#if !CIRCUITPY
|
||||
STATIC const mp_map_elem_t ulab_globals_table[] = {
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR___name__), MP_OBJ_NEW_QSTR(MP_QSTR_ulab) },
|
||||
{ MP_ROM_QSTR(MP_QSTR___version__), MP_ROM_PTR(&ulab_version_obj) },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_array), (mp_obj_t)&ulab_ndarray_type },
|
||||
#if ULAB_LINALG_MODULE
|
||||
{ MP_ROM_QSTR(MP_QSTR_linalg), MP_ROM_PTR(&ulab_linalg_module) },
|
||||
#endif
|
||||
#if ULAB_VECTORISE_MODULE
|
||||
{ MP_ROM_QSTR(MP_QSTR_vector), MP_ROM_PTR(&ulab_vectorise_module) },
|
||||
#endif
|
||||
#if ULAB_NUMERICAL_MODULE
|
||||
{ MP_ROM_QSTR(MP_QSTR_numerical), MP_ROM_PTR(&ulab_numerical_module) },
|
||||
#endif
|
||||
#if ULAB_POLY_MODULE
|
||||
{ MP_ROM_QSTR(MP_QSTR_poly), MP_ROM_PTR(&ulab_poly_module) },
|
||||
#endif
|
||||
#if ULAB_FFT_MODULE
|
||||
{ MP_ROM_QSTR(MP_QSTR_fft), MP_ROM_PTR(&ulab_fft_module) },
|
||||
#endif
|
||||
#if ULAB_FILTER_MODULE
|
||||
{ MP_ROM_QSTR(MP_QSTR_filter), MP_ROM_PTR(&ulab_filter_module) },
|
||||
#endif
|
||||
#if ULAB_EXTRAS_MODULE
|
||||
{ MP_ROM_QSTR(MP_QSTR_extras), MP_ROM_PTR(&ulab_extras_module) },
|
||||
#endif
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_size), (mp_obj_t)&linalg_size_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_inv), (mp_obj_t)&linalg_inv_obj },
|
||||
{ MP_ROM_QSTR(MP_QSTR_dot), (mp_obj_t)&linalg_dot_obj },
|
||||
{ MP_ROM_QSTR(MP_QSTR_zeros), (mp_obj_t)&linalg_zeros_obj },
|
||||
{ MP_ROM_QSTR(MP_QSTR_ones), (mp_obj_t)&linalg_ones_obj },
|
||||
{ MP_ROM_QSTR(MP_QSTR_eye), (mp_obj_t)&linalg_eye_obj },
|
||||
{ MP_ROM_QSTR(MP_QSTR_det), (mp_obj_t)&linalg_det_obj },
|
||||
{ MP_ROM_QSTR(MP_QSTR_eig), (mp_obj_t)&linalg_eig_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_acos), (mp_obj_t)&vectorise_acos_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_acosh), (mp_obj_t)&vectorise_acosh_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_asin), (mp_obj_t)&vectorise_asin_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_asinh), (mp_obj_t)&vectorise_asinh_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_atan), (mp_obj_t)&vectorise_atan_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_atanh), (mp_obj_t)&vectorise_atanh_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_ceil), (mp_obj_t)&vectorise_ceil_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_cos), (mp_obj_t)&vectorise_cos_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_erf), (mp_obj_t)&vectorise_erf_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_erfc), (mp_obj_t)&vectorise_erfc_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_exp), (mp_obj_t)&vectorise_exp_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_expm1), (mp_obj_t)&vectorise_expm1_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_floor), (mp_obj_t)&vectorise_floor_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_gamma), (mp_obj_t)&vectorise_gamma_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_lgamma), (mp_obj_t)&vectorise_lgamma_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_log), (mp_obj_t)&vectorise_log_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_log10), (mp_obj_t)&vectorise_log10_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_log2), (mp_obj_t)&vectorise_log2_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_sin), (mp_obj_t)&vectorise_sin_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_sinh), (mp_obj_t)&vectorise_sinh_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_sqrt), (mp_obj_t)&vectorise_sqrt_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_tan), (mp_obj_t)&vectorise_tan_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_tanh), (mp_obj_t)&vectorise_tanh_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_linspace), (mp_obj_t)&numerical_linspace_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_sum), (mp_obj_t)&numerical_sum_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_mean), (mp_obj_t)&numerical_mean_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_std), (mp_obj_t)&numerical_std_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_min), (mp_obj_t)&numerical_min_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_max), (mp_obj_t)&numerical_max_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_argmin), (mp_obj_t)&numerical_argmin_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_argmax), (mp_obj_t)&numerical_argmax_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_roll), (mp_obj_t)&numerical_roll_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_flip), (mp_obj_t)&numerical_flip_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_diff), (mp_obj_t)&numerical_diff_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_sort), (mp_obj_t)&numerical_sort_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_argsort), (mp_obj_t)&numerical_argsort_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_polyval), (mp_obj_t)&poly_polyval_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_polyfit), (mp_obj_t)&poly_polyfit_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_fft), (mp_obj_t)&fft_fft_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_ifft), (mp_obj_t)&fft_ifft_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_spectrum), (mp_obj_t)&fft_spectrum_obj },
|
||||
// class constants
|
||||
{ MP_ROM_QSTR(MP_QSTR_uint8), MP_ROM_INT(NDARRAY_UINT8) },
|
||||
{ MP_ROM_QSTR(MP_QSTR_int8), MP_ROM_INT(NDARRAY_INT8) },
|
||||
|
|
@ -95,10 +179,9 @@ STATIC MP_DEFINE_CONST_DICT (
|
|||
ulab_globals_table
|
||||
);
|
||||
|
||||
mp_obj_module_t ulab_user_cmodule = {
|
||||
const mp_obj_module_t ulab_user_cmodule = {
|
||||
.base = { &mp_type_module },
|
||||
.globals = (mp_obj_dict_t*)&mp_module_ulab_globals,
|
||||
};
|
||||
|
||||
MP_REGISTER_MODULE(MP_QSTR_ulab, ulab_user_cmodule, MODULE_ULAB_ENABLED);
|
||||
#endif
|
||||
|
|
|
|||
36
code/ulab.h
36
code/ulab.h
|
|
@ -1,36 +0,0 @@
|
|||
|
||||
/*
|
||||
* This file is part of the micropython-ulab project,
|
||||
*
|
||||
* https://github.com/v923z/micropython-ulab
|
||||
*
|
||||
* The MIT License (MIT)
|
||||
*
|
||||
* Copyright (c) 2019-2020 Zoltán Vörös
|
||||
*/
|
||||
|
||||
#ifndef __ULAB__
|
||||
#define __ULAB__
|
||||
|
||||
// vectorise (all functions) takes approx. 3 kB of flash space
|
||||
#define ULAB_VECTORISE_MODULE (1)
|
||||
|
||||
// linalg adds around 6 kB
|
||||
#define ULAB_LINALG_MODULE (1)
|
||||
|
||||
// poly is approx. 2.5 kB
|
||||
#define ULAB_POLY_MODULE (1)
|
||||
|
||||
// numerical is about 12 kB
|
||||
#define ULAB_NUMERICAL_MODULE (1)
|
||||
|
||||
// FFT costs about 2 kB of flash space
|
||||
#define ULAB_FFT_MODULE (1)
|
||||
|
||||
// the filter module takes about 1 kB of flash space
|
||||
#define ULAB_FILTER_MODULE (1)
|
||||
|
||||
// user-defined modules
|
||||
#define ULAB_EXTRAS_MODULE (0)
|
||||
|
||||
#endif
|
||||
100
code/vectorise.c
100
code/vectorise.c
|
|
@ -1,4 +1,3 @@
|
|||
|
||||
/*
|
||||
* This file is part of the micropython-ulab project,
|
||||
*
|
||||
|
|
@ -8,7 +7,7 @@
|
|||
*
|
||||
* Copyright (c) 2019-2020 Zoltán Vörös
|
||||
*/
|
||||
|
||||
|
||||
#include <math.h>
|
||||
#include <stdio.h>
|
||||
#include <stdlib.h>
|
||||
|
|
@ -16,20 +15,20 @@
|
|||
#include "py/binary.h"
|
||||
#include "py/obj.h"
|
||||
#include "py/objarray.h"
|
||||
#include "compat.h"
|
||||
#include "vectorise.h"
|
||||
|
||||
#ifndef MP_PI
|
||||
#define MP_PI MICROPY_FLOAT_CONST(3.14159265358979323846)
|
||||
#endif
|
||||
|
||||
#if ULAB_VECTORISE_MODULE
|
||||
mp_obj_t vectorise_generic_vector(mp_obj_t o_in, mp_float_t (*f)(mp_float_t)) {
|
||||
// Return a single value, if o_in is not iterable
|
||||
if(mp_obj_is_float(o_in) || MP_OBJ_IS_INT(o_in)) {
|
||||
if(mp_obj_is_float(o_in) || mp_obj_is_integer(o_in)) {
|
||||
return mp_obj_new_float(f(mp_obj_get_float(o_in)));
|
||||
}
|
||||
mp_float_t x;
|
||||
if(MP_OBJ_IS_TYPE(o_in, &ulab_ndarray_type)) {
|
||||
if(mp_obj_is_type(o_in, &ulab_ndarray_type)) {
|
||||
ndarray_obj_t *source = MP_OBJ_TO_PTR(o_in);
|
||||
ndarray_obj_t *ndarray = create_new_ndarray(source->m, source->n, NDARRAY_FLOAT);
|
||||
mp_float_t *dataout = (mp_float_t *)ndarray->array->items;
|
||||
|
|
@ -45,8 +44,8 @@ mp_obj_t vectorise_generic_vector(mp_obj_t o_in, mp_float_t (*f)(mp_float_t)) {
|
|||
ITERATE_VECTOR(mp_float_t, source, dataout);
|
||||
}
|
||||
return MP_OBJ_FROM_PTR(ndarray);
|
||||
} else if(MP_OBJ_IS_TYPE(o_in, &mp_type_tuple) || MP_OBJ_IS_TYPE(o_in, &mp_type_list) ||
|
||||
MP_OBJ_IS_TYPE(o_in, &mp_type_range)) { // i.e., the input is a generic iterable
|
||||
} else if(mp_obj_is_type(o_in, &mp_type_tuple) || mp_obj_is_type(o_in, &mp_type_list) ||
|
||||
mp_obj_is_type(o_in, &mp_type_range)) { // i.e., the input is a generic iterable
|
||||
mp_obj_array_t *o = MP_OBJ_TO_PTR(o_in);
|
||||
ndarray_obj_t *out = create_new_ndarray(1, o->len, NDARRAY_FLOAT);
|
||||
mp_float_t *dataout = (mp_float_t *)out->array->items;
|
||||
|
|
@ -62,113 +61,26 @@ mp_obj_t vectorise_generic_vector(mp_obj_t o_in, mp_float_t (*f)(mp_float_t)) {
|
|||
return mp_const_none;
|
||||
}
|
||||
|
||||
|
||||
MATH_FUN_1(acos, acos);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_acos_obj, vectorise_acos);
|
||||
|
||||
MATH_FUN_1(acosh, acosh);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_acosh_obj, vectorise_acosh);
|
||||
|
||||
MATH_FUN_1(asin, asin);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_asin_obj, vectorise_asin);
|
||||
|
||||
MATH_FUN_1(asinh, asinh);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_asinh_obj, vectorise_asinh);
|
||||
|
||||
MATH_FUN_1(atan, atan);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_atan_obj, vectorise_atan);
|
||||
|
||||
MATH_FUN_1(atanh, atanh);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_atanh_obj, vectorise_atanh);
|
||||
|
||||
MATH_FUN_1(ceil, ceil);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_ceil_obj, vectorise_ceil);
|
||||
|
||||
MATH_FUN_1(cos, cos);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_cos_obj, vectorise_cos);
|
||||
|
||||
MATH_FUN_1(cosh, cosh);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_cosh_obj, vectorise_cosh);
|
||||
|
||||
MATH_FUN_1(erf, erf);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_erf_obj, vectorise_erf);
|
||||
|
||||
MATH_FUN_1(erfc, erfc);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_erfc_obj, vectorise_erfc);
|
||||
|
||||
MATH_FUN_1(exp, exp);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_exp_obj, vectorise_exp);
|
||||
|
||||
MATH_FUN_1(expm1, expm1);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_expm1_obj, vectorise_expm1);
|
||||
|
||||
MATH_FUN_1(floor, floor);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_floor_obj, vectorise_floor);
|
||||
|
||||
MATH_FUN_1(gamma, tgamma);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_gamma_obj, vectorise_gamma);
|
||||
|
||||
MATH_FUN_1(lgamma, lgamma);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_lgamma_obj, vectorise_lgamma);
|
||||
|
||||
MATH_FUN_1(log, log);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_log_obj, vectorise_log);
|
||||
|
||||
MATH_FUN_1(log10, log10);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_log10_obj, vectorise_log10);
|
||||
|
||||
MATH_FUN_1(log2, log2);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_log2_obj, vectorise_log2);
|
||||
|
||||
MATH_FUN_1(sin, sin);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_sin_obj, vectorise_sin);
|
||||
|
||||
MATH_FUN_1(sinh, sinh);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_sinh_obj, vectorise_sinh);
|
||||
|
||||
MATH_FUN_1(sqrt, sqrt);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_sqrt_obj, vectorise_sqrt);
|
||||
|
||||
MATH_FUN_1(tan, tan);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_tan_obj, vectorise_tan);
|
||||
|
||||
MATH_FUN_1(tanh, tanh);
|
||||
MP_DEFINE_CONST_FUN_OBJ_1(vectorise_tanh_obj, vectorise_tanh);
|
||||
|
||||
#if !CIRCUITPY
|
||||
STATIC const mp_rom_map_elem_t ulab_vectorise_globals_table[] = {
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR___name__), MP_OBJ_NEW_QSTR(MP_QSTR_vector) },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_acos), (mp_obj_t)&vectorise_acos_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_acosh), (mp_obj_t)&vectorise_acosh_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_asin), (mp_obj_t)&vectorise_asin_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_asinh), (mp_obj_t)&vectorise_asinh_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_atan), (mp_obj_t)&vectorise_atan_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_atanh), (mp_obj_t)&vectorise_atanh_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_ceil), (mp_obj_t)&vectorise_ceil_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_cos), (mp_obj_t)&vectorise_cos_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_erf), (mp_obj_t)&vectorise_erf_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_erfc), (mp_obj_t)&vectorise_erfc_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_exp), (mp_obj_t)&vectorise_exp_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_expm1), (mp_obj_t)&vectorise_expm1_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_floor), (mp_obj_t)&vectorise_floor_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_gamma), (mp_obj_t)&vectorise_gamma_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_lgamma), (mp_obj_t)&vectorise_lgamma_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_log), (mp_obj_t)&vectorise_log_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_log10), (mp_obj_t)&vectorise_log10_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_log2), (mp_obj_t)&vectorise_log2_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_sin), (mp_obj_t)&vectorise_sin_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_sinh), (mp_obj_t)&vectorise_sinh_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_sqrt), (mp_obj_t)&vectorise_sqrt_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_tan), (mp_obj_t)&vectorise_tan_obj },
|
||||
{ MP_OBJ_NEW_QSTR(MP_QSTR_tanh), (mp_obj_t)&vectorise_tanh_obj },
|
||||
};
|
||||
|
||||
STATIC MP_DEFINE_CONST_DICT(mp_module_ulab_vectorise_globals, ulab_vectorise_globals_table);
|
||||
|
||||
mp_obj_module_t ulab_vectorise_module = {
|
||||
.base = { &mp_type_module },
|
||||
.globals = (mp_obj_dict_t*)&mp_module_ulab_vectorise_globals,
|
||||
};
|
||||
#endif
|
||||
|
||||
#endif
|
||||
|
|
|
|||
|
|
@ -1,4 +1,3 @@
|
|||
|
||||
/*
|
||||
* This file is part of the micropython-ulab project,
|
||||
*
|
||||
|
|
@ -6,18 +5,37 @@
|
|||
*
|
||||
* The MIT License (MIT)
|
||||
*
|
||||
* Copyright (c) 2019-2020 Zoltán Vörös
|
||||
* Copyright (c) 2019 Zoltán Vörös
|
||||
*/
|
||||
|
||||
|
||||
#ifndef _VECTORISE_
|
||||
#define _VECTORISE_
|
||||
|
||||
#include "ulab.h"
|
||||
#include "ndarray.h"
|
||||
|
||||
#if ULAB_VECTORISE_MODULE
|
||||
|
||||
mp_obj_module_t ulab_vectorise_module;
|
||||
mp_obj_t vectorise_acos(mp_obj_t );
|
||||
mp_obj_t vectorise_acosh(mp_obj_t );
|
||||
mp_obj_t vectorise_asin(mp_obj_t );
|
||||
mp_obj_t vectorise_asinh(mp_obj_t );
|
||||
mp_obj_t vectorise_atan(mp_obj_t );
|
||||
mp_obj_t vectorise_atanh(mp_obj_t );
|
||||
mp_obj_t vectorise_ceil(mp_obj_t );
|
||||
mp_obj_t vectorise_cos(mp_obj_t );
|
||||
mp_obj_t vectorise_erf(mp_obj_t );
|
||||
mp_obj_t vectorise_erfc(mp_obj_t );
|
||||
mp_obj_t vectorise_exp(mp_obj_t );
|
||||
mp_obj_t vectorise_expm1(mp_obj_t );
|
||||
mp_obj_t vectorise_floor(mp_obj_t );
|
||||
mp_obj_t vectorise_gamma(mp_obj_t );
|
||||
mp_obj_t vectorise_lgamma(mp_obj_t );
|
||||
mp_obj_t vectorise_log(mp_obj_t );
|
||||
mp_obj_t vectorise_log10(mp_obj_t );
|
||||
mp_obj_t vectorise_log2(mp_obj_t );
|
||||
mp_obj_t vectorise_sin(mp_obj_t );
|
||||
mp_obj_t vectorise_sinh(mp_obj_t );
|
||||
mp_obj_t vectorise_sqrt(mp_obj_t );
|
||||
mp_obj_t vectorise_tan(mp_obj_t );
|
||||
mp_obj_t vectorise_tanh(mp_obj_t );
|
||||
|
||||
#define ITERATE_VECTOR(type, source, out) do {\
|
||||
type *input = (type *)(source)->array->items;\
|
||||
|
|
@ -29,7 +47,6 @@ mp_obj_module_t ulab_vectorise_module;
|
|||
#define MATH_FUN_1(py_name, c_name) \
|
||||
mp_obj_t vectorise_ ## py_name(mp_obj_t x_obj) { \
|
||||
return vectorise_generic_vector(x_obj, MICROPY_FLOAT_C_FUN(c_name)); \
|
||||
}
|
||||
}
|
||||
|
||||
#endif
|
||||
#endif
|
||||
|
|
|
|||
|
|
@ -22,7 +22,7 @@ copyright = '2019, Zoltán Vörös'
|
|||
author = 'Zoltán Vörös'
|
||||
|
||||
# The full version, including alpha/beta/rc tags
|
||||
release = '0.32'
|
||||
release = '0.26'
|
||||
|
||||
|
||||
# -- General configuration ---------------------------------------------------
|
||||
|
|
|
|||
|
|
@ -1,10 +1,10 @@
|
|||
Introduction
|
||||
============
|
||||
|
||||
In the `last
|
||||
chapter <https://micropython-usermod.readthedocs.io/en/latest/usermods_15.html>`__
|
||||
of the usermod documentation, I mentioned that I have another story, for
|
||||
another day. The day has come, so here is my story.
|
||||
In
|
||||
https://micropython-usermod.readthedocs.io/en/latest/usermods_14.html, I
|
||||
mentioned that I have another story, for another day. The day has come,
|
||||
so here is my story.
|
||||
|
||||
Enter ulab
|
||||
----------
|
||||
|
|
@ -68,11 +68,9 @@ The main points of ``ulab`` are
|
|||
- polynomial fits to numerical data
|
||||
- fast Fourier transforms
|
||||
|
||||
At the time of writing this manual (for version 0.32), the library adds
|
||||
At the time of writing this manual (for version 0.26), the library adds
|
||||
approximately 30 kB of extra compiled code to the micropython
|
||||
(pyboard.v.11) firmware. However, if you are tight with flash space, you
|
||||
can easily shave off a couple of kB. See the section on `customising
|
||||
ulab <#Custom_builds>`__.
|
||||
(pyboard.v.11) firmware.
|
||||
|
||||
Resources and legal matters
|
||||
---------------------------
|
||||
|
|
@ -145,35 +143,6 @@ can always be queried as
|
|||
|
||||
If you find a bug, please, include this number in your report!
|
||||
|
||||
Customising ``ulab``
|
||||
--------------------
|
||||
|
||||
``ulab`` implements a great number of functions, and it is quite
|
||||
possible that you do not need all of them in a particular application.
|
||||
If you want to save some flash space, you can easily exclude arbitrary
|
||||
functions from the firmware. The
|
||||
`https://github.com/v923z/micropython-ulab/blob/master/code/ulab.h <ulab.h>`__
|
||||
header file contains a pre-processor flag for all functions in ``ulab``.
|
||||
The default setting is 1 for each of them, but if you change that to 0,
|
||||
the corresponding function will not be part of the compiled firmware.
|
||||
|
||||
The first couple of lines of the file look like this
|
||||
|
||||
.. code:: c
|
||||
|
||||
// vectorise (all functions) takes approx. 3 kB of flash space
|
||||
#define ULAB_VECTORISE_ACOS (1)
|
||||
#define ULAB_VECTORISE_ACOSH (1)
|
||||
#define ULAB_VECTORISE_ASIN (1)
|
||||
#define ULAB_VECTORISE_ASINH (1)
|
||||
#define ULAB_VECTORISE_ATAN (1)
|
||||
#define ULAB_VECTORISE_ATANH (1)
|
||||
|
||||
In order to simplify navigation in the file, each flag begins with
|
||||
``ULAB_``, continues with the sub-module, where the function itself is
|
||||
implemented, and ends with the function’s name. Each section displays a
|
||||
hint as to how much space you can save by un-setting the flag.
|
||||
|
||||
Basic ndarray operations
|
||||
------------------------
|
||||
|
||||
|
|
@ -197,10 +166,14 @@ Methods of ndarrays
|
|||
|
||||
`.reshape <#.reshape>`__
|
||||
|
||||
`.rawsize\*\* <#.rawsize>`__
|
||||
|
||||
`.transpose <#.transpose>`__
|
||||
|
||||
`.flatten\*\* <#.flatten>`__
|
||||
|
||||
`.asbytearray <#.asbytearray>`__
|
||||
|
||||
Matrix methods
|
||||
--------------
|
||||
|
||||
|
|
@ -266,11 +239,6 @@ FFT routines
|
|||
|
||||
`spectrum\*\* <#spectrum>`__
|
||||
|
||||
Filter functions
|
||||
----------------
|
||||
|
||||
`convolve <#convolve>`__
|
||||
|
||||
ndarray, the basic container
|
||||
============================
|
||||
|
||||
|
|
@ -424,8 +392,8 @@ Methods of ndarrays
|
|||
.shape
|
||||
~~~~~~
|
||||
|
||||
The ``.shape`` method (property) returns a 2-tuple with the number of
|
||||
rows, and columns.
|
||||
The ``.shape`` method returns a 2-tuple with the number of rows, and
|
||||
columns.
|
||||
|
||||
.. code::
|
||||
|
||||
|
|
@ -435,11 +403,11 @@ rows, and columns.
|
|||
|
||||
a = np.array([1, 2, 3, 4], dtype=np.int8)
|
||||
print("a:\n", a)
|
||||
print("shape of a:", a.shape)
|
||||
print("shape of a:", a.shape())
|
||||
|
||||
b= np.array([[1, 2], [3, 4]], dtype=np.int8)
|
||||
print("\nb:\n", b)
|
||||
print("shape of b:", b.shape)
|
||||
print("shape of b:", b.shape())
|
||||
|
||||
.. parsed-literal::
|
||||
|
||||
|
|
@ -455,74 +423,6 @@ rows, and columns.
|
|||
|
||||
|
||||
|
||||
.size
|
||||
~~~~~
|
||||
|
||||
The ``.size`` method (property) returns an integer with the number of
|
||||
elements in the array.
|
||||
|
||||
.. code::
|
||||
|
||||
# code to be run in micropython
|
||||
|
||||
import ulab as np
|
||||
|
||||
a = np.array([1, 2, 3], dtype=np.int8)
|
||||
print("a:\n", a)
|
||||
print("size of a:", a.size)
|
||||
|
||||
b= np.array([[1, 2], [3, 4]], dtype=np.int8)
|
||||
print("\nb:\n", b)
|
||||
print("size of b:", b.size)
|
||||
|
||||
.. parsed-literal::
|
||||
|
||||
a:
|
||||
array([1, 2, 3], dtype=int8)
|
||||
size of a: 3
|
||||
|
||||
b:
|
||||
array([[1, 2],
|
||||
[3, 4]], dtype=int8)
|
||||
size of b: 4
|
||||
|
||||
|
||||
|
||||
|
||||
.itemsize
|
||||
~~~~~~~~~
|
||||
|
||||
The ``.itemsize`` method (property) returns an integer with the siz
|
||||
enumber of elements in the array.
|
||||
|
||||
.. code::
|
||||
|
||||
# code to be run in micropython
|
||||
|
||||
import ulab as np
|
||||
|
||||
a = np.array([1, 2, 3], dtype=np.int8)
|
||||
print("a:\n", a)
|
||||
print("itemsize of a:", a.itemsize)
|
||||
|
||||
b= np.array([[1, 2], [3, 4]], dtype=np.float)
|
||||
print("\nb:\n", b)
|
||||
print("itemsize of b:", b.itemsize)
|
||||
|
||||
.. parsed-literal::
|
||||
|
||||
a:
|
||||
array([1, 2, 3], dtype=int8)
|
||||
itemsize of a: 1
|
||||
|
||||
b:
|
||||
array([[1.0, 2.0],
|
||||
[3.0, 4.0]], dtype=float)
|
||||
itemsize of b: 8
|
||||
|
||||
|
||||
|
||||
|
||||
.reshape
|
||||
~~~~~~~~
|
||||
|
||||
|
|
@ -559,6 +459,41 @@ consistent with the old, a ``ValueError`` exception will be raised.
|
|||
|
||||
|
||||
|
||||
.rawsize
|
||||
~~~~~~~~
|
||||
|
||||
The ``rawsize`` method of the ``ndarray`` returns a 5-tuple with the
|
||||
following data
|
||||
|
||||
1. number of rows
|
||||
2. number of columns
|
||||
3. length of the storage (should be equal to the product of 1. and 2.)
|
||||
4. length of the data storage in bytes
|
||||
5. datum size in bytes (1 for ``uint8``/``int8``, 2 for
|
||||
``uint16``/``int16``, and 4, or 8 for ``floats``, see `ndarray, the
|
||||
basic container <#ndarray,-the-basic-container>`__)
|
||||
|
||||
**WARNING:** ``rawsize`` is a ``ulab``-only method; it has no equivalent
|
||||
in ``numpy``.
|
||||
|
||||
.. code::
|
||||
|
||||
# code to be run in micropython
|
||||
|
||||
import ulab as np
|
||||
|
||||
a = np.array([1, 2, 3, 4], dtype=np.float)
|
||||
print("a: \t\t", a)
|
||||
print("rawsize of a: \t", a.rawsize())
|
||||
|
||||
.. parsed-literal::
|
||||
|
||||
a: array([1.0, 2.0, 3.0, 4.0], dtype=float)
|
||||
rawsize of a: (1, 4, 4, 16, 4)
|
||||
|
||||
|
||||
|
||||
|
||||
.flatten
|
||||
~~~~~~~~
|
||||
|
||||
|
|
@ -600,6 +535,83 @@ verbatim copy of the contents.
|
|||
|
||||
|
||||
|
||||
.asbytearray
|
||||
~~~~~~~~~~~~
|
||||
|
||||
The contents of an ``ndarray`` can be accessed directly by calling the
|
||||
``.asbytearray`` method. This will simply return a pointer to the
|
||||
underlying flat ``array`` object, which can then be manipulated
|
||||
directly.
|
||||
|
||||
**WARNING:** ``asbytearray`` is a ``ulab``-only method; it has no
|
||||
equivalent in ``numpy``.
|
||||
|
||||
In the example below, note the difference between ``a``, and ``buffer``:
|
||||
while both are designated as an array, you recognise the micropython
|
||||
array from the fact that it prints the typecode (``b`` in this
|
||||
particular case). The ``ndarray``, on the other hand, prints out the
|
||||
``dtype`` (``int8`` here).
|
||||
|
||||
.. code::
|
||||
|
||||
# code to be run in micropython
|
||||
|
||||
import ulab as np
|
||||
|
||||
a = np.array([1, 2, 3, 4], dtype=np.int8)
|
||||
print('a: ', a)
|
||||
buffer = a.asbytearray()
|
||||
print("array content:", buffer)
|
||||
buffer[1] = 123
|
||||
print("array content:", buffer)
|
||||
|
||||
.. parsed-literal::
|
||||
|
||||
a: array([1, 2, 3, 4], dtype=int8)
|
||||
array content: array('b', [1, 2, 3, 4])
|
||||
array content: array('b', [1, 123, 3, 4])
|
||||
|
||||
|
||||
|
||||
|
||||
This in itself wouldn’t be very interesting, but since ``buffer`` is a
|
||||
proper micropython ``array``, we can pass it to functions that can
|
||||
employ the buffer protocol. E.g., all the ``ndarray`` facilities can be
|
||||
applied to the results of timed ADC conversions.
|
||||
|
||||
.. code::
|
||||
|
||||
# code to be run in micropython
|
||||
|
||||
import pyb
|
||||
import ulab as np
|
||||
|
||||
n = 100
|
||||
|
||||
adc = pyb.ADC(pyb.Pin.board.X19)
|
||||
tim = pyb.Timer(6, freq=10)
|
||||
|
||||
a = np.array([0]*n, dtype=np.uint8)
|
||||
buffer = a.asbytearray()
|
||||
adc.read_timed(buffer, tim)
|
||||
|
||||
print("ADC results:\t", a)
|
||||
print("mean of results:\t", np.mean(a))
|
||||
print("std of results:\t", np.std(a))
|
||||
|
||||
.. parsed-literal::
|
||||
|
||||
ADC results: array([48, 2, 2, ..., 0, 0, 0], dtype=uint8)
|
||||
mean of results: 1.22
|
||||
std of results: 4.744639
|
||||
|
||||
|
||||
|
||||
Likewise, data can be read directly into ``ndarray``\ s from other
|
||||
interfaces, e.g., SPI, I2C etc, and also, by laying bare the
|
||||
``ndarray``, we can pass results of ``ulab`` computations to anything
|
||||
that can read from a buffer.
|
||||
|
||||
.transpose
|
||||
~~~~~~~~~~
|
||||
|
||||
|
|
@ -2977,40 +2989,6 @@ Y_2(k) &=& -\frac{i}{2}\left(Y(k) - Y^*(N-k)\right)
|
|||
:math:`Y_1, Y_2`, and :math:`Y`, respectively, are the Fourier
|
||||
transforms of :math:`y_1, y_2`, and :math:`y = y_1 + iy_2`.
|
||||
|
||||
Filter routines
|
||||
===============
|
||||
|
||||
numpy:
|
||||
https://docs.scipy.org/doc/numpy/reference/generated/numpy.convolve.html
|
||||
|
||||
convolve
|
||||
--------
|
||||
|
||||
Returns the discrete, linear convolution of two one-dimensional
|
||||
sequences.
|
||||
|
||||
Only the ``full`` mode is supported, and the ``mode`` named parameter is
|
||||
not accepted. Note that all other modes can be had by slicing a ``full``
|
||||
result.
|
||||
|
||||
.. code::
|
||||
|
||||
# code to be run in micropython
|
||||
|
||||
import ulab as np
|
||||
|
||||
x = np.array((1,2,3))
|
||||
y = np.array((1,10,100,1000))
|
||||
|
||||
print(np.convolve(x, y))
|
||||
|
||||
.. parsed-literal::
|
||||
|
||||
array([1.0, 12.0, 123.0, 1230.0, 2300.0, 3000.0], dtype=float)
|
||||
|
||||
|
||||
|
||||
|
||||
Extending ulab
|
||||
==============
|
||||
|
||||
|
|
|
|||
|
|
@ -1,70 +1,4 @@
|
|||
|
||||
Tue, 18 Feb 2020
|
||||
|
||||
version 0.34.0
|
||||
|
||||
split ulab into multiple modules
|
||||
|
||||
Sun, 16 Feb 2020
|
||||
|
||||
version 0.33.2
|
||||
|
||||
moved properties into ndarray_properties.h, implemented pointer arithmetic in fft.c to save some time
|
||||
|
||||
Fri, 14 Feb 2020
|
||||
|
||||
version 0.33.1
|
||||
|
||||
added the __name__attribute to all sub-modules
|
||||
|
||||
Thu, 13 Feb 2020
|
||||
|
||||
version 0.33.0
|
||||
|
||||
sub-modules are now proper sub-modules of ulab
|
||||
|
||||
Mon, 17 Feb 2020
|
||||
|
||||
version 0.32.1
|
||||
|
||||
temporary fix for issue #40
|
||||
|
||||
Tue, 11 Feb 2020
|
||||
|
||||
version 0.32.0
|
||||
|
||||
added itemsize, size and shape attributes to ndarrays, and removed rawsize
|
||||
|
||||
Mon, 10 Feb 2020
|
||||
|
||||
version 0.31.0
|
||||
|
||||
removed asbytearray, and added buffer protocol to ndarrays, fixed bad error in filter.c
|
||||
|
||||
Sun, 09 Feb 2020
|
||||
|
||||
version 0.30.2
|
||||
|
||||
fixed slice_length in ndarray.c
|
||||
|
||||
Sat, 08 Feb 2020
|
||||
|
||||
version 0.30.1
|
||||
|
||||
fixed typecode error, added variable inspection, and replaced ternary operators in filter.c
|
||||
|
||||
Fri, 07 Feb 2020
|
||||
|
||||
version 0.30.0
|
||||
|
||||
ulab functions can arbitrarily be excluded from the firmware via the ulab.h configuration file
|
||||
|
||||
Thu, 06 Feb 2020
|
||||
|
||||
version 0.27.0
|
||||
|
||||
add convolve, the start of a 'filter' functionality group
|
||||
|
||||
Wed, 29 Jan 2020
|
||||
|
||||
version 0.26.7
|
||||
|
|
|
|||
|
|
@ -24,11 +24,11 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"execution_count": 23,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-02-11T19:06:35.427133Z",
|
||||
"start_time": "2020-02-11T19:06:35.418598Z"
|
||||
"end_time": "2019-11-05T16:01:28.817558Z",
|
||||
"start_time": "2019-11-05T16:01:28.810704Z"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
|
|
@ -66,7 +66,7 @@
|
|||
"author = 'Zoltán Vörös'\n",
|
||||
"\n",
|
||||
"# The full version, including alpha/beta/rc tags\n",
|
||||
"release = '0.32'\n",
|
||||
"release = '0.26'\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# -- General configuration ---------------------------------------------------\n",
|
||||
|
|
@ -120,11 +120,10 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-02-11T20:30:43.287360Z",
|
||||
"start_time": "2020-02-11T20:30:40.308932Z"
|
||||
"start_time": "2019-11-06T17:37:29.723Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
|
|
@ -293,11 +292,11 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-02-16T14:53:49.098172Z",
|
||||
"start_time": "2020-02-16T14:53:49.093201Z"
|
||||
"end_time": "2019-11-04T20:57:36.730820Z",
|
||||
"start_time": "2019-11-04T20:57:36.723446Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
|
|
@ -311,11 +310,11 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-02-16T14:53:53.396267Z",
|
||||
"start_time": "2020-02-16T14:53:53.375754Z"
|
||||
"end_time": "2019-11-04T20:57:38.576560Z",
|
||||
"start_time": "2019-11-04T20:57:38.521927Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
|
|
@ -384,20 +383,13 @@
|
|||
"ip.register_magics(PyboardMagic)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## pyboard"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 111,
|
||||
"execution_count": 520,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-02-16T18:36:59.172039Z",
|
||||
"start_time": "2020-02-16T18:36:59.144651Z"
|
||||
"end_time": "2019-10-20T06:48:01.610725Z",
|
||||
"start_time": "2019-10-20T06:48:00.856261Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
|
|
@ -409,11 +401,11 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 110,
|
||||
"execution_count": 501,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-02-16T17:34:35.250747Z",
|
||||
"start_time": "2020-02-16T17:34:35.241871Z"
|
||||
"end_time": "2019-10-19T13:36:42.010602Z",
|
||||
"start_time": "2019-10-19T13:36:42.003900Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
|
|
@ -485,7 +477,7 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"In the [last chapter](https://micropython-usermod.readthedocs.io/en/latest/usermods_15.html) of the usermod documentation, I mentioned that I have another story, for another day. The day has come, so here is my story.\n",
|
||||
"In https://micropython-usermod.readthedocs.io/en/latest/usermods_14.html, I mentioned that I have another story, for another day. The day has come, so here is my story.\n",
|
||||
"\n",
|
||||
"## Enter ulab\n",
|
||||
"\n",
|
||||
|
|
@ -513,7 +505,7 @@
|
|||
"- polynomial fits to numerical data\n",
|
||||
"- fast Fourier transforms\n",
|
||||
"\n",
|
||||
"At the time of writing this manual (for version 0.32), the library adds approximately 30 kB of extra compiled code to the micropython (pyboard.v.11) firmware. However, if you are tight with flash space, you can easily shave off a couple of kB. See the section on [customising ulab](#Custom_builds).\n",
|
||||
"At the time of writing this manual (for version 0.26), the library adds approximately 30 kB of extra compiled code to the micropython (pyboard.v.11) firmware. \n",
|
||||
"\n",
|
||||
"## Resources and legal matters\n",
|
||||
"\n",
|
||||
|
|
@ -576,42 +568,6 @@
|
|||
"If you find a bug, please, include this number in your report!"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Customising `ulab`\n",
|
||||
"\n",
|
||||
"`ulab` implements a great number of functions, which are organised in sub-modules. E.g., functions related to Fourier transforms are located in the `ulab.fft` sub-module, so you would import `fft` as\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"import ulab\n",
|
||||
"from ulab import fft\n",
|
||||
"```\n",
|
||||
"by which point you can get the FFT of your data by calling `fft.fft(...)`. \n",
|
||||
"\n",
|
||||
"The idea of such grouping of functions and methods is to provide a means for granularity: It is quite possible that you do not need all functions in a particular application. If you want to save some flash space, you can easily exclude arbitrary sub-modules from the firmware. The [https://github.com/v923z/micropython-ulab/blob/master/code/ulab.h](ulab.h) header file contains a pre-processor flag for each sub-module. The default setting is 1 for each of them, but if you change that to 0, the corresponding sub-module will not be part of the compiled firmware. \n",
|
||||
"\n",
|
||||
"The first couple of lines of the file look like this\n",
|
||||
"\n",
|
||||
"```c\n",
|
||||
"// vectorise (all functions) takes approx. 3 kB of flash space\n",
|
||||
"#define ULAB_VECTORISE_MODULE (1)\n",
|
||||
"\n",
|
||||
"// linalg adds around 6 kB\n",
|
||||
"#define ULAB_LINALG_MODULE (1)\n",
|
||||
"\n",
|
||||
"// poly is approx. 2.5 kB\n",
|
||||
"#define ULAB_POLY_MODULE (1)\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"In order to simplify navigation in the header, each flag begins with `ULAB_`, and continues with the name of the sub-module. This name is also the `.c` file, where the sub-module is implemented. So, e.g., the linear algebra routines can be found in `linalg.c`, and the corresponding compiler flag is `ULAB_LINALG_MODULE`. Each section displays a hint as to how much space you can save by un-setting the flag.\n",
|
||||
"\n",
|
||||
"At first, having to import everything in this way might appear to be overly complicated, but there is a very good reason behind all this: you can find out at the time of importing, whether a function is part of your `ulab` firmware, or not. The alternative, namely, that you do not have to import anything beyond `ulab`, could be catastrophic: you would learn only at run time that a particular function is not in the firmware, and that is most probably too late.\n",
|
||||
"\n",
|
||||
"The standard sub-modules, `vector`, `linalg`, `numerical`, `poly`, and `fft` are all `numpy`-compatible. User-defined functions that accept `ndarray`s as their argument should be implemented in the `extra` sub-module, or its sub-modules."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
|
|
@ -637,10 +593,14 @@
|
|||
"\n",
|
||||
"[.reshape](#.reshape)\n",
|
||||
"\n",
|
||||
"[.rawsize<sup>**</sup>](#.rawsize)\n",
|
||||
"\n",
|
||||
"[.transpose](#.transpose)\n",
|
||||
"\n",
|
||||
"[.flatten<sup>**</sup>](#.flatten)\n",
|
||||
"\n",
|
||||
"[.asbytearray](#.asbytearray)\n",
|
||||
"\n",
|
||||
"## Matrix methods\n",
|
||||
"\n",
|
||||
"[size](#size)\n",
|
||||
|
|
@ -699,11 +659,7 @@
|
|||
"\n",
|
||||
"[ifft<sup>**</sup>](#ifft)\n",
|
||||
"\n",
|
||||
"[spectrum<sup>**</sup>](#spectrum)\n",
|
||||
"\n",
|
||||
"## Filter functions\n",
|
||||
"\n",
|
||||
"[convolve](#convolve)"
|
||||
"[spectrum<sup>**</sup>](#spectrum)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -712,13 +668,13 @@
|
|||
"source": [
|
||||
"# ndarray, the basic container\n",
|
||||
"\n",
|
||||
"The `ndarray` is the underlying container of numerical data. It is derived from micropython's own `array` object, but has a great number of extra features starting with how it can be initialised, which operations can be done on it, and which functions can accept it as an argument. One important property of an `ndarray` is that it is also a proper `micropython` iterable.\n",
|
||||
"The `ndarray` is the underlying container of numerical data. It is derived from micropython's own `array` object, but has a great number of extra features starting with how it can be initialised, how operations can be done on it, and which functions can accept it as an argument.\n",
|
||||
"\n",
|
||||
"Since the `ndarray` is a binary container, it is also compact, meaning that it takes only a couple of bytes of extra RAM in addition to what is required for storing the numbers themselves. `ndarray`s are also type-aware, i.e., one can save RAM by specifying a data type, and using the smallest reasonable one. Five such types are defined, namely `uint8`, `int8`, which occupy a single byte of memory per datum, `uint16`, and `int16`, which occupy two bytes per datum, and `float`, which occupies four or eight bytes per datum. The precision/size of the `float` type depends on the definition of `mp_float_t`. Some platforms, e.g., the PYBD, implement `double`s, but some, e.g., the pyboard.v.11, don't. You can find out, what type of float your particular platform implements by looking at the output of the [.itemsize](#.itemsize) class property.\n",
|
||||
"Since the `ndarray` is a binary container, it is also compact, meaning that it takes only a couple of bytes of extra RAM in addition to what is required for storing the numbers themselves. `ndarray`s are also type-aware, i.e., one can save RAM by specifying a data type, and using the smallest reasonable one. Five such types are defined, namely `uint8`, `int8`, which occupy a single byte of memory per datum, `uint16`, and `int16`, which occupy two bytes per datum, and `float`, which occupies four or eight bytes per datum. The precision/size of the `float` type depends on the definition of `mp_float_t`. Some platforms, e.g., the PYBD, implement `double`s, but some, e.g., the pyboard.v.11, don't. You can find out, what type of float your particular platform implements by looking at the output of the [.rawsize](#.rawsize) class method.\n",
|
||||
"\n",
|
||||
"On the following pages, we will see how one can work with `ndarray`s. Those familiar with `numpy` should find that the nomenclature and naming conventions of `numpy` are adhered to as closely as possible. I will point out the few differences, where necessary.\n",
|
||||
"\n",
|
||||
"For the sake of comparison, in addition to the `ulab` code snippets, sometimes the equivalent `numpy` code is also presented. You can find out, where the snippet is supposed to run by looking at its first line, the header.\n",
|
||||
"For the sake of comparison, in addition to `ulab` code snippets, sometimes the equivalent `numpy` code is also presented. You can find out, where the snippet is supposed to run by looking at its first line, the header.\n",
|
||||
"\n",
|
||||
"Hint: you can easily port existing `numpy` code, if you `import ulab as np`."
|
||||
]
|
||||
|
|
@ -886,16 +842,16 @@
|
|||
"source": [
|
||||
"### .shape\n",
|
||||
"\n",
|
||||
"The `.shape` method (property) returns a 2-tuple with the number of rows, and columns."
|
||||
"The `.shape` method returns a 2-tuple with the number of rows, and columns."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"execution_count": 283,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-02-11T19:01:40.377272Z",
|
||||
"start_time": "2020-02-11T19:01:40.364448Z"
|
||||
"end_time": "2019-10-16T15:30:33.810628Z",
|
||||
"start_time": "2019-10-16T15:30:33.796088Z"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
|
|
@ -923,111 +879,11 @@
|
|||
"\n",
|
||||
"a = np.array([1, 2, 3, 4], dtype=np.int8)\n",
|
||||
"print(\"a:\\n\", a)\n",
|
||||
"print(\"shape of a:\", a.shape)\n",
|
||||
"print(\"shape of a:\", a.shape())\n",
|
||||
"\n",
|
||||
"b= np.array([[1, 2], [3, 4]], dtype=np.int8)\n",
|
||||
"print(\"\\nb:\\n\", b)\n",
|
||||
"print(\"shape of b:\", b.shape)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### .size\n",
|
||||
"\n",
|
||||
"The `.size` method (property) returns an integer with the number of elements in the array."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-02-11T06:32:22.721112Z",
|
||||
"start_time": "2020-02-11T06:32:22.713111Z"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"a:\n",
|
||||
" array([1, 2, 3], dtype=int8)\n",
|
||||
"size of a: 3\n",
|
||||
"\n",
|
||||
"b:\n",
|
||||
" array([[1, 2],\n",
|
||||
"\t [3, 4]], dtype=int8)\n",
|
||||
"size of b: 4\n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%%micropython -unix 1\n",
|
||||
"\n",
|
||||
"import ulab as np\n",
|
||||
"\n",
|
||||
"a = np.array([1, 2, 3], dtype=np.int8)\n",
|
||||
"print(\"a:\\n\", a)\n",
|
||||
"print(\"size of a:\", a.size)\n",
|
||||
"\n",
|
||||
"b= np.array([[1, 2], [3, 4]], dtype=np.int8)\n",
|
||||
"print(\"\\nb:\\n\", b)\n",
|
||||
"print(\"size of b:\", b.size)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### .itemsize\n",
|
||||
"\n",
|
||||
"The `.itemsize` method (property) returns an integer with the siz enumber of elements in the array."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-02-11T19:05:04.296601Z",
|
||||
"start_time": "2020-02-11T19:05:04.280669Z"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"a:\n",
|
||||
" array([1, 2, 3], dtype=int8)\n",
|
||||
"itemsize of a: 1\n",
|
||||
"\n",
|
||||
"b:\n",
|
||||
" array([[1.0, 2.0],\n",
|
||||
"\t [3.0, 4.0]], dtype=float)\n",
|
||||
"itemsize of b: 8\n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%%micropython -unix 1\n",
|
||||
"\n",
|
||||
"import ulab as np\n",
|
||||
"\n",
|
||||
"a = np.array([1, 2, 3], dtype=np.int8)\n",
|
||||
"print(\"a:\\n\", a)\n",
|
||||
"print(\"itemsize of a:\", a.itemsize)\n",
|
||||
"\n",
|
||||
"b= np.array([[1, 2], [3, 4]], dtype=np.float)\n",
|
||||
"print(\"\\nb:\\n\", b)\n",
|
||||
"print(\"itemsize of b:\", b.itemsize)"
|
||||
"print(\"shape of b:\", b.shape())"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -1078,6 +934,54 @@
|
|||
"print('a (1 by 16):', a.reshape((1, 16)))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### .rawsize\n",
|
||||
"\n",
|
||||
"The `rawsize` method of the `ndarray` returns a 5-tuple with the following data\n",
|
||||
"\n",
|
||||
"1. number of rows\n",
|
||||
"2. number of columns\n",
|
||||
"3. length of the storage (should be equal to the product of 1. and 2.)\n",
|
||||
"4. length of the data storage in bytes \n",
|
||||
"5. datum size in bytes (1 for `uint8`/`int8`, 2 for `uint16`/`int16`, and 4, or 8 for `floats`, see [ndarray, the basic container](#ndarray,-the-basic-container))\n",
|
||||
"\n",
|
||||
"**WARNING:** `rawsize` is a `ulab`-only method; it has no equivalent in `numpy`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 510,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2019-10-19T17:44:26.983908Z",
|
||||
"start_time": "2019-10-19T17:44:26.764912Z"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"a: \t\t array([1.0, 2.0, 3.0, 4.0], dtype=float)\n",
|
||||
"rawsize of a: \t (1, 4, 4, 16, 4)\n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%%micropython -unix 1\n",
|
||||
"\n",
|
||||
"import ulab as np\n",
|
||||
"\n",
|
||||
"a = np.array([1, 2, 3, 4], dtype=np.float)\n",
|
||||
"print(\"a: \\t\\t\", a)\n",
|
||||
"print(\"rawsize of a: \\t\", a.rawsize())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
|
|
@ -1131,6 +1035,109 @@
|
|||
"print(\"b flattened (F): \\t\", b.flatten(order='F'))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### .asbytearray\n",
|
||||
"\n",
|
||||
"The contents of an `ndarray` can be accessed directly by calling the `.asbytearray` method. This will simply return a pointer to the underlying flat `array` object, which can then be manipulated directly.\n",
|
||||
"\n",
|
||||
"**WARNING:** `asbytearray` is a `ulab`-only method; it has no equivalent in `numpy`.\n",
|
||||
"\n",
|
||||
"In the example below, note the difference between `a`, and `buffer`: while both are designated as an array, you recognise the micropython array from the fact that it prints the typecode (`b` in this particular case). The `ndarray`, on the other hand, prints out the `dtype` (`int8` here)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 211,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2019-11-01T14:25:39.008074Z",
|
||||
"start_time": "2019-11-01T14:25:38.876546Z"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"a: array([1, 2, 3, 4], dtype=int8)\n",
|
||||
"array content: array('b', [1, 2, 3, 4])\n",
|
||||
"array content: array('b', [1, 123, 3, 4])\n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%%micropython -unix 1\n",
|
||||
"\n",
|
||||
"import ulab as np\n",
|
||||
"\n",
|
||||
"a = np.array([1, 2, 3, 4], dtype=np.int8)\n",
|
||||
"print('a: ', a)\n",
|
||||
"buffer = a.asbytearray()\n",
|
||||
"print(\"array content:\", buffer)\n",
|
||||
"buffer[1] = 123\n",
|
||||
"print(\"array content:\", buffer)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"This in itself wouldn't be very interesting, but since `buffer` is a proper micropython `array`, we can pass it to functions that can employ the buffer protocol. E.g., all the `ndarray` facilities can be applied to the results of timed ADC conversions."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 563,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2019-10-20T07:29:00.153589Z",
|
||||
"start_time": "2019-10-20T07:28:50.210383Z"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"ADC results:\t array([48, 2, 2, ..., 0, 0, 0], dtype=uint8)\n",
|
||||
"mean of results:\t 1.22\n",
|
||||
"std of results:\t 4.744639\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%%micropython -pyboard 1\n",
|
||||
"\n",
|
||||
"import pyb\n",
|
||||
"import ulab as np\n",
|
||||
"\n",
|
||||
"n = 100\n",
|
||||
"\n",
|
||||
"adc = pyb.ADC(pyb.Pin.board.X19)\n",
|
||||
"tim = pyb.Timer(6, freq=10)\n",
|
||||
"\n",
|
||||
"a = np.array([0]*n, dtype=np.uint8)\n",
|
||||
"buffer = a.asbytearray()\n",
|
||||
"adc.read_timed(buffer, tim)\n",
|
||||
"\n",
|
||||
"print(\"ADC results:\\t\", a)\n",
|
||||
"print(\"mean of results:\\t\", np.mean(a))\n",
|
||||
"print(\"std of results:\\t\", np.std(a))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Likewise, data can be read directly into `ndarray`s from other interfaces, e.g., SPI, I2C etc, and also, by laying bare the `ndarray`, we can pass results of `ulab` computations to anything that can read from a buffer."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
|
|
@ -3981,11 +3988,11 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 114,
|
||||
"execution_count": 458,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-02-16T18:38:07.294862Z",
|
||||
"start_time": "2020-02-16T18:38:07.233842Z"
|
||||
"end_time": "2019-10-19T13:07:43.168629Z",
|
||||
"start_time": "2019-10-19T13:07:43.130341Z"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
|
|
@ -3993,13 +4000,13 @@
|
|||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"real part:\t array([5119.996, -5.004663, -5.004798, ..., -5.005482, -5.005643, -5.006577], dtype=float)\r\n",
|
||||
"\r\n",
|
||||
"imaginary part:\t array([0.0, 1631.333, 815.659, ..., -543.764, -815.6588, -1631.333], dtype=float)\r\n",
|
||||
"\r\n",
|
||||
"real part:\t array([5119.996, -5.004663, -5.004798, ..., -5.005482, -5.005643, -5.006577], dtype=float)\r\n",
|
||||
"\r\n",
|
||||
"imaginary part:\t array([0.0, 1631.333, 815.659, ..., -543.764, -815.6588, -1631.333], dtype=float)\r\n",
|
||||
"real part:\t array([5119.996, -5.004663, -5.004798, ..., -5.005482, -5.005643, -5.006577], dtype=float)\n",
|
||||
"\n",
|
||||
"imaginary part:\t array([0.0, 1631.333, 815.659, ..., -543.764, -815.6588, -1631.333], dtype=float)\n",
|
||||
"\n",
|
||||
"real part:\t array([5119.996, -5.004663, -5.004798, ..., -5.005482, -5.005643, -5.006577], dtype=float)\n",
|
||||
"\n",
|
||||
"imaginary part:\t array([0.0, 1631.333, 815.659, ..., -543.764, -815.6588, -1631.333], dtype=float)\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
|
|
@ -4008,20 +4015,16 @@
|
|||
"%%micropython -pyboard 1\n",
|
||||
"\n",
|
||||
"import ulab as np\n",
|
||||
"from ulab import numerical\n",
|
||||
"from ulab import vector\n",
|
||||
"from ulab import fft\n",
|
||||
"from ulab import linalg\n",
|
||||
"\n",
|
||||
"x = numerical.linspace(0, 10, num=1024)\n",
|
||||
"y = vector.sin(x)\n",
|
||||
"z = linalg.zeros(len(x))\n",
|
||||
"x = np.linspace(0, 10, num=1024)\n",
|
||||
"y = np.sin(x)\n",
|
||||
"z = np.zeros(len(x))\n",
|
||||
"\n",
|
||||
"a, b = fft.fft(x)\n",
|
||||
"a, b = np.fft(x)\n",
|
||||
"print('real part:\\t', a)\n",
|
||||
"print('\\nimaginary part:\\t', b)\n",
|
||||
"\n",
|
||||
"c, d = fft.fft(x, z)\n",
|
||||
"c, d = np.fft(x, z)\n",
|
||||
"print('\\nreal part:\\t', c)\n",
|
||||
"print('\\nimaginary part:\\t', d)"
|
||||
]
|
||||
|
|
@ -4265,56 +4268,6 @@
|
|||
"where $N$ is the length of $y_1$, and $Y_1, Y_2$, and $Y$, respectively, are the Fourier transforms of $y_1, y_2$, and $y = y_1 + iy_2$."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Filter routines"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"numpy: https://docs.scipy.org/doc/numpy/reference/generated/numpy.convolve.html\n",
|
||||
"\n",
|
||||
"## convolve\n",
|
||||
"Returns the discrete, linear convolution of two one-dimensional sequences.\n",
|
||||
"\n",
|
||||
"Only the ``full`` mode is supported, and the ``mode`` named parameter is not accepted. Note that all other modes can be had by slicing a ``full`` result."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-02-10T18:46:06.538207Z",
|
||||
"start_time": "2020-02-10T18:46:06.525851Z"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"array([1.0, 12.0, 123.0, 1230.0, 2300.0, 3000.0], dtype=float)\n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%%micropython -unix 1\n",
|
||||
"\n",
|
||||
"import ulab as np\n",
|
||||
"\n",
|
||||
"x = np.array((1,2,3))\n",
|
||||
"y = np.array((1,10,100,1000))\n",
|
||||
"\n",
|
||||
"print(np.convolve(x, y))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
|
|
@ -4360,7 +4313,7 @@
|
|||
" NDARRAY_FLOAT = 'd',\n",
|
||||
"};\n",
|
||||
"```\n",
|
||||
"The ambiguity is caused by the fact that not all platforms implement `double`, and there one has to take `float`s. But you haven't actually got to be concerned by this, because at the very beginning of `ndarray.h`, this is already taken care of: the pre-processor figures out, what the `float` implementation of the hardware platform is, and defines the `NDARRAY_FLOAT` typecode accordingly. All you have to keep in mind is that wherever you would use `float` or `double`, you have to use `mp_float_t`. That type is defined in `py/mpconfig.h` of the micropython code base.\n",
|
||||
"The ambiguity is caused by the fact that not all platforms implement `double`, and there one has to take `float`s. But you haven't actually got to be concerned by this, because at the very beginning of `ndarray.h`, this is already taken care of: the preprocessor figures out, what the `float` implementation of the hardware platform is, and defines the `NDARRAY_FLOAT` typecode accordingly. All you have to keep in mind is that wherever you would use `float` or `double`, you have to use `mp_float_t`. That type is defined in `py/mpconfig.h` of the micropython code base.\n",
|
||||
"\n",
|
||||
"Therefore, a 4-by-5 matrix of type float can be created as\n",
|
||||
"\n",
|
||||
|
|
|
|||
6152
docs/ulab.ipynb
6152
docs/ulab.ipynb
File diff suppressed because it is too large
Load diff
|
|
@ -1,2 +0,0 @@
|
|||
from ulab import linalg
|
||||
print(linalg.eye(3))
|
||||
|
|
@ -1,3 +0,0 @@
|
|||
array([[1.0, 0.0, 0.0],
|
||||
[0.0, 1.0, 0.0],
|
||||
[0.0, 0.0, 1.0]], dtype=float)
|
||||
Loading…
Reference in a new issue